The present disclosure relates to health monitoring. More
particularly the present disclosure relates to an apparatus and method for diagnosis medical condition of a user using X-Ray images automatically without any assistance of medical practitioners.
BACKGROUND
[0002] Background description includes information that may be useful in
understanding the present invention. It is not an admission that any of the
information provided herein is prior art or relevant to the presently claimed
invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Whole world has been facing the deadly and highly contagious
disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic in 2020. Corona virus affects the respiratory system including airways and lungs. COVID-19, which is also known as Sars-coV-2 is a pathogen, attacks by affecting the lung tissues which in-turn causes pneumonia or lung failure in the patients which may lead to death in some cases. Similar to COVID-19 Disease, pulmonary disease such as COPD, Emphysema, Chronic Bronchitis, Asthma and Pneumonia are lung diseases that blocks airflow and make it difficult to breath for the patient and causing severe internal damage to the lungs which prove to be fatal in most cases if not treated properly at the preliminary stage.
[0004] Chest X-Ray is one of the important, non-invasive clinical adjuncts
that play an essential role in the preliminary investigation of similar pulmonary abnormalities such as pneumonia, acute respiratory distress syndrome (ARDS) etc. Chest X-Ray acts as an alternative screening modality for the detection or validation of the related diagnosis, where the X-Ray images are interpreted by expert radiologists to look for infectious lesions associated with pulmonary diseases. This is a very rigorous process and might have chance of human errors. Also the patients in remote areas face much tribulation because of scarcity of

good medical experts. Thus there is a need of dedicated automatic handheld
device, which performs such diagnosis task accurately, automatically and easily.
[0005] Conventional system and methods requires an expert radiologist
manually capture and check radiology images to detect diseases, thus proved to be inefficient and time consuming, as in clinical practice, and the radiologists only have the anatomical information of X-ray images.
[0006] There is, therefore, a need of an improved apparatus and method
for analyzing X-ray images automatically without need of any medical practitioners, and accurately analyzing the X-ray images to detect various diseases at an early stage.
OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one
embodiment herein satisfies are as listed herein below.
[0008] It is an object of the present disclosure to provide an apparatus for
detection of a variety of pulmonary diseases through the analysis of X-Ray
images.
[0009] It is an object of the present disclosure to provide a handheld
apparatus for detection of pulmonary diseases through the analysis of X-Ray
images by applying conventional machine learning and deep learning (DL)
approaches.
[0010] It is an object of the present disclosure to provide an apparatus for
detection of pulmonary diseases through the analysis of X-Ray images
automatically, which is efficient.
[0011] It is an object of the present disclosure to provide an apparatus for
detection of pulmonary diseases through the analysis of X-Ray images
automatically, which is easy to use.
[0012] It is an object of the present disclosure to provide an apparatus
which automatically obtain X-ray images of chest of a user, and analyses the X-
ray images to find disease.

[0013] It is an object of the present disclosure to provide an automatic
handheld apparatus, which performs diagnosis task accurately.
[0014] It is an object of the proposed disclosure to provide an apparatus
which is used at hospitals for regular monitoring of patients and timely
medication.
[0015] It is an object of the proposed disclosure to provide an apparatus
which gives 24x7 connectivity with government authorities and hospitals for
making more effective availability of medical facilities in remote area.
SUMMARY
[0016] The present disclosure generally relates to health monitoring, and
specifically relates to an apparatus and method for diagnosis a medical condition of a user using X-Ray images automatically without any assistance of medical practitioners.
[0017] An aspect of the present disclosure pertains to an apparatus to
detect medical abnormalities using X-ray images. The apparatus may include an
input unit to receive the X-ray images as input, and correspondingly generate a
first set of signals. A processing unit may be operatively configured with the
input unit, and the processing unit may be including one or more processors
coupled with a memory, the memory storing instructions executable by the one or
more processors configured to receive, the first set of signals, extract a plurality of
health attributes from the received first set of signals, compare the extracted
plurality of health attributes with a set of pre-defined threshold range to determine
medical abnormalities in body of a user using deep learning algorithms. Generate
and transmit a second set of signals corresponding to the medical abnormalities to
one or more mobile computing devices using a communication unit.
[0018] In an aspect, the second set of signals may include information
relating to plurality of health attributes and the medical abnormalities found in the user;
[0019] In an aspect the processing unit may be further configured to
transmit a third set of signals to a server, and the server may be configured to

store information associated with the plurality of users, and enable authorised
entities to access information using the associated mobile computing device.
[0020] In an aspect, the input unit may include but not limited to an X-ray
machine to capture X-ray images of chest of a user in real-time, one or more input
ports 118 to receive input from external devices, and an image capturing unit 104
to capture images of physical X-ray placed on a pre-defined position.
[0021] In an aspect, the plurality of health attributes may include but not
limited to breathing, lung infection, cough, bone structure, and heart shape.
[0022] In an aspect, medical abnormalities may include but not limited to
chronic obstructive pulmonary disease, pulmonary fibrosis, pneumonia, COVID, asthma, lung disease and lung cancer.
[0023] In an aspect, the communication unit may include but not limited
to Wireless Fidelity (Wi-Fi) Module, Bluetooth Module, Li-Fi Module, Wireless Local Area Network (WLAN), and ZigBee.
[0024] In an aspect, the deep learning algorithms may be selected from a
group consisting of, but not limited to support vector machines, decision trees,
artificial neural networks, and convolutional neural networks.
[0025] In an aspect, the apparatus may be a handheld apparatus to enables
the user to use the apparatus easily.
[0026] In an aspect, a display unit may be operatively coupled with the
processing unit to display information relating to plurality of health attributes and
the medical abnormalities found in the user. The display unit may be selected
from a group consisting of, but not limited to light emitting diode (LED), liquid
crystal display (LCD), organic light emitting diode (OLED), and LED matrix.
[0027] Another aspect of the present disclosure pertains to a method for
detecting medical abnormalities using X-ray images, the method may including, receiving, by an input unit, X-ray images of a user, extracting, by one or more processors, a plurality of health attributes from the received X-ray images. Determining, by the one or more processors, medical abnormalities in the user by analysing the extracted plurality of health attributes displaying, by the one or more processors, the medical abnormalities found in the user on a display unit and

one or more mobile computing devices. Also storing, at a server, the plurality of health attributes and the medical abnormalities of the user, and enabling authorized entities to access stored information, using the associated mobile computing devices.
[0028] Various objects, features, aspects and advantages of the inventive
subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.
BRIEF DESCRIPTION OF DRAWINGS
[0029] The following is a detailed description of embodiments of the
disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0030] In the following description, numerous specific details are set forth
in order to provide a thorough understanding of embodiments of the present
invention. It will be apparent to one skilled in the art that embodiments of the
present invention may be practiced without some of these specific details.
[0031] FIG. 1 illustrates an exemplary representations of an apparatus for
detection of medical abnormalities using X-ray images, in accordance with an embodiment of the present disclosure.
[0032] FIG 2 illustrates an exemplary block diagram of an apparatus for
detection of medical abnormalities using X-ray images, in accordance with an embodiment of the present disclosure.
[0033] FIG. 3 illustrates an exemplary functional components of a
processing unit of the proposed apparatus, in accordance with an embodiment of the present disclosure.

[0034] FIG. 4 illustrates an exemplary method for detection of medical
abnormalities using X-ray images, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0035] The following is a detailed description of embodiments of the
disclosure depicted in the accompanying drawings. The embodiments are in such
detail as to clearly communicate the disclosure. However, the amount of detail
offered is not intended to limit the anticipated variations of embodiments; on the
contrary, the intention is to cover all modifications, equivalents, and alternatives
falling within the scope of the present disclosure as defined by the appended
claims.
[0036] In the following description, numerous specific details are set forth
in order to provide a thorough understanding of embodiments of the present
invention. It will be apparent to one skilled in the art that embodiments of the
present invention may be practiced without some of these specific details.
[0037] Embodiments of the present disclosure relates to health
monitoring, and more particularly relates to an apparatus and method for
diagnosis medical condition of a user using X-Ray images without any human
intervention.
[0038] The present disclosure elaborates upon an apparatus for
determining medical abnormalities in user using X-ray images. The X-ray images
can be received as input by an input unit provided in the apparatus, and received
X-ray images can be analysed using deep learning algorithms to detect medical
abnormalities in body of the user instantly. The proposed apparatus is portable,
the user can obtain X-ray images by himself by following pre-defined
instructions, or existing X-ray can be fed to the input unit to check medical
abnormalities.
[0039] In an embodiment, medical abnormalities found in the user by
examining X-ray images can be displayed on a display unit provided on the
apparatus, and can be transmitted to one or more mobile computing devices

associated with the user. Also, the extracted attributes and medical abnormalities found in the user can be transmitted to a server to store the information, which can be accessed by authorized entities such as medical practitioners when required. Further, the collected information can be used to train the model used to analyse the X-ray images to provide more accurate results.
[0040] FIG. 1 illustrates an exemplary representation of the apparatus 100,
in according to some embodiments of the present disclosure. The apparatus 100
can include an input unit 102 to obtain X-ray images, a processing unit 110 to
process and analyse the obtained X-ray images and determining medical
abnormalities (also referred as diseases) in associated user, and an output unit 122
configured to transmit determined information to a display unit 124.
[0041] In accordance with an embodiment of the disclosure, the input unit
102 can include an X-ray machine to capture X-ray images of chest of the user. Instructions can be pre-stored in a memory 116, and when the user actuate the X-ray machine (not shown), instructions can be displayed on a display unit 124, which displays steps to use the X-ray machine which can enable the user to obtain X-ray by himself, or by assistance of any other person such as family member, and friends, but without medical practitioners.
[0042] In accordance with another embodiment of the disclosure, the input
unit 102 can include an X-ray feeder 106 along with illumination units 108. The X-ray feeder can be a cavity to receive physical X-ray, and upon receiving X-ray the illumination units 108 can be illuminated to provide light inside the input unit 102. An image capturing unit 104 can be configured in the input unit 102, which upon actuation capture images of the physical x-ray positioned in the X-ray feeder 106. For example, the X-ray feeder 106 can include a cover (not shown) which can be opened to receive physical X-ray, and the cover can be closed to capture images of the received physical X-ray.
[0043] In accordance with some other embodiment of the disclosure, the
input unit 102 can include one or more input ports 118. The one or more input ports 118 can be of USB-type, C-Type, 3 pin type, USB-C type, micro, pin and the likes which enables the user to couple the apparatus 100 with external devices

such as mobile phone, computer, laptop, and the likes to receive earlier captured X-ray images.
[0044] In an embodiment, the X-ray images received from any medium
such as physical X-ray, soft copy of X-ray, and captured through inbuilt X-ray machine can be transmitted to a processing unit 110. The processing unit 110 can include an image processing module 112 to analyse the received images, by extracting health attributes from the received images. The health attributes can include but not limited to breathing, lung infection, cough, bone structure, and heart shape.
[0045] In an embodiment, the extracted health attributes can be
transmitted to a classification model based on deep learning model 114 (also referred as classification and training unit 114), where various deep learning techniques such as but not limited to support vector machines, decision trees, artificial neural networks, and convolutional neural networks can be applied on the extracted health attributes to determine medical abnormalities. The medical abnormalities can include but not limited to chronic obstructive pulmonary disease, pulmonary fibrosis, pneumonia, COVID, asthma, lung disease and lung cancer.
[0046] In an embodiment, the medical abnormalities found in the X-ray
images can be transmitted to an output unit 122. The output unit 122 can include a display unit 124 to display information on the apparatus 100, which enables the user to get outcomes of the X-ray images without any medical practitioners. The display unit 124 can be selected from a group consisting of but not limited to light emitting diode (LED), liquid crystal display (LCD), organic light emitting diode (OLED), LED matrix. Also, the output unit 122 can include output ports 126, which can be configured to couple the apparatus with external devices such as printer to get physical print in the form of a report.
[0047] In an exemplary embodiment, the apparatus 100 can include one or
more buttons (not shown) which enables the user to choose and perform one or more operations such as back, next, ok, print, but not limited to these. Also, these

buttons can be provided in the display unit to enable the user to easy access the apparatus 100.
[0048] In another exemplary embodiment, the collected information can
be transmitted to Medical facilities (such as concerned hospitals/Clinics) as well
as Government authorities (in case of COVID positive) for statistical purpose.
[0049] Referring to FIG. 2, a block diagram of the apparatus 100 is
disclosed, the apparatus 100 can include an input unit 102 to obtain X-ray images,
and transmits the obtained images to a processing unit 110. The processing unit
110 can be configured to analyse the received X-ray images by applying various
deep learning techniques, and medical abnormalities can be detected by analysing
health attributes extracted from the received X-ray images. The apparatus 100 can
include a display unit 124 to display information such as extracted health
attributes and medical abnormalities found in the user. Further, a communication
unit 202 can be configured within the apparatus 100, the communication unit 202
can be communicatively coupled with the processing unit 110.
[0050] In an embodiment, the communication unit can include but not
limited to Wireless Fidelity (Wi-Fi) Module, Bluetooth, Li-Fi, Wireless Local Area Network (WLAN), ZigBee, and GSM module.
[0051] In accordance with an embodiment of the disclosure, the
communication unit 202 can be communicatively coupled with one or more mobile computing devices 204 (collectively referred as mobile computing devices 204, and individually referred as mobile computing device 204), and a server 208. The one or more mobile computing devices 204 can include but not limited to a laptop, phone, tablet, computer, and laptop etc. The apparatus 100 can interact
with the users 206-1, 206-2 206-N (collectively referred as users 206, and
individually referred as user 206 hereinafter) through the associated mobile computing devices 204 or through an application residing on the mobile computing device 204.
[0052] In an embodiment, one or more entities 210, such as medical
practitioners, hospital staff, nurses, hospital, and pharmacist can be given access to check the information of the users 206 from the server 208. The server 208 can

store all information of users 206 such as personal details (i.e. name, age, medical history, and phone number), X-ray images, and Medical abnormalities found, and the likes, and the user 206 can provide access to any preferred entity 210 when required. To access the server, the entity can enter a PIN, OTP, etc., which can prevent unauthorized access of the user's information.
[0053] In an exemplary embodiment, when the user is not feeling well, the
user himself or any other family person can take X-ray from the machine provided
in the apparatus 100, and the apparatus 100 can check and provide disease related
to lung, bones, COVID, and the likes instantly, and the user can take online
prescriptions from the doctor by providing access to the doctor.
[0054] In another exemplary embodiment, the server 208 can provide all
time (24x7) connectivity with government authorities and hospitals for making
more effective availability of medical facilities in remote area.
[0055] In an embodiment, the apparatus 100 can include oximeter sensor
and thermal scanner to collect oxygen level and body temperature of the user
respectively and can be transmitted to the processing unit 110. The processing
unit 110 can be configured to compare collected values of oxygen level and body
temperature can be with pre-defined threshold value stored in memory 116, and
upon detection of any of the value beyond the threshold values, the user can be
notified by displaying a notification on the display unit 124, further the
notification can be transmitted to associated mobile computing devices 204
[0056] As illustrated in FIG. 3, a processing unit 110 can include one or
more processor(s) 302. The one or more processor(s) 302 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 302 can be configured to fetch and execute computer readable instructions stored in a memory 116 of the processing unit 110. The memory 116 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 116 can include any non-transitory storage device

including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the likes.
[0057] In an embodiment, the processing unit 110 can also include an
interface(s) 304. The interface(s) 304 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 304 may facilitate communication of apparatus 100. The interface(s) 304 may also provide a communication pathway for one or more components of the apparatus 100. Examples of such components include, but are not limited to, processing engine(s) 308 and database 310.
[0058] In an embodiment, a processing engine(s) 308 can be implemented
as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 308. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 308 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 308 may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 308. In such examples, the processing unit 110 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to processing unit 110 and the processing resource. In other examples, the processing engine(s) 110 may be implemented by electronic circuitry. A database 310 can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 308.
[0059] In an embodiment, the processing engine(s) 308 can include an
extraction unit 312, a comparison unit 314, a classification and training unit 114, a

signal generation unit 316, and other unit(s) 318. The other unit(s) 318 can implement functionalities that supplement applications or functions performed by the apparatus 100 or the processing engine(s) 308.
[0060] In an embodiment, the database 310 can include data that is either
stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 308.
[0061] It would be appreciated that units being described are only
exemplary units and any other unit or sub-unit may be included as part of the apparatus 100. These units too may be merged or divided into super- units or sub-units as may be configured.
[0062] In an embodiment, the processing unit 110 can be configured to
receive a first set of signals from an input unit 102 in electric form, where the first set of signals pertain a health attributes of a user, and further transmits the first set of signals to the extraction unit 312. The extraction unit 312 can be configured to extract a fourth set of signals from the first set of signals, where the fourth set of signals pertain to characteristics of health attributes of the user. The health attributes can include but not limited to breathing, lung infection, cough, bone structure, and heart shape.
[0063] In an embodiment, the comparison unit 314 can be configured to
compare the extracted characteristics of the health attributes with a set of pre-defined threshold range , and further transmitted the information to the classification and training unit 114 in machine readable form or binary form, where the classification and training unit 114 classify the characteristics and correspondingly the signal generation unit 316 can generate and transmit a second set of signals to one or more mobile computing devices 204 using a communication unit 202, where the second set of signals can pertain information relating to medical abnormalities such as chronic obstructive pulmonary disease, pulmonary fibrosis, pneumonia, COVID, asthma, lung disease, lung cancer, and etc.
[0064] In an embodiment, the signal generation unit 316 can be further
configured to generate and transmit a third set of signals based on the outcome of

analysis, and third set of signals can be transmitted to a server 208. The server 208 can be configured to store information associated with the various users, and enable authorised entities 210 to access information using the associated mobile computing device 204 of particular user 206.
[0065] In an exemplary embodiment, when the apparatus 100 found any
of the medial abnormality on risk, the user can visit hospital at earliest, or can
share generated report with the doctor to take medications online.
[0066] In an embodiment, the classification and training unit 114 can be
configured to receive the extracted characteristics of health attributes, from the extraction unit 312 in machine readable form or binary form and update and train the classification and training unit 114 based on extracted characteristics of health attributes. In another embodiment, the classification and training unit 114 can be trained and updated based on the received health attributes. A deep leaning model can be trained based on the received health attributes and analysed information where the deep leaning model can be stored in the database 310. In yet another embodiment, once the dataset is trained correctly, a deep learning algorithm can be configured to perform repetitive, and routine tasks within a shorter period of time.
[0067] In an embodiment, the classification and training unit 114 can be
configured to store the many users health attributes recorded over a period of time such as for week, month etc., for trend analysis and prediction of future health risk. Also, the classification and training unit 114 can be configured to store a set of training datasets to train a machine learning model for determining medical abnormalities automatically. The plurality of training datasets can include historical information related to breathing, lung infection, cough, bone structure, heart shape and the likes of a group of healthy people and a group of people suffering from some diseases.
[0068] In an exemplary embodiment, the processing engine 308 can be
further configured in the form of a learning engine like the following, but not limited to machine learning algorithms and deep learning algorithms. In an exemplary embodiment, the processing engine 308 can include deep learning

algorithms such as but not limited to support vector machines, decision trees,
artificial neural networks, and convolutional neural networks.
[0069] In an exemplary embodiment, the deep learning model can be
trained using advanced deep architectures such as but not limited to AlexNet
VGG Net, GoogleNet, ResNet, ResNeXt, RCNN (Region based CNN),
SqueezeNet, SegNet, and Generative Adversarial Network GAN to enhance
accuracy.
[0070] As illustrated in FIG. 4, a method for detecting medical
abnormalities from the X-ray images of chest is disclosed, at step 402, the method
400 can include receiving by an input unit 102, X-ray images of a user, and the
input unit 102 can be configured to receive X-ray images in a soft copy, physical
form, or can capture images by itself using a X-ray machine.
[0071] At step 404, the method 400 can include extracting, by the one or
more processors, health attributes from the received X-ray images, and the health
attributes can include but not limited to breathing, lung infection, cough, bone
structure, and heart shape.
[0072] At step 406, the method 400 can include determining, by the one or
more processors, medical abnormalities in the user by analysing the
characteristics of the extracted health attributes, and the medical abnormalities
can include but not limited to chronic obstructive pulmonary disease, pulmonary
fibrosis, pneumonia, COVID, asthma, lung disease and lung cancer.
[0073] At step 408, the method 400 can include displaying, by the one or
more processors, the medical abnormalities found in the user on a display unit 124
and one or more mobile computing devices 204 of associated user 206.
[0074] At step 410, the method 400 can include storing, at a server 208,
the plurality of health attributes and the medical abnormalities of the user, and
enabling authorized entities to access stored information, using the associated
mobile computing devices 204.
[0075] Moreover, in interpreting the specification, all terms should be
interpreted in the broadest possible manner consistent with the context. In
particular, the terms "comprises" and "comprising" should be interpreted as

referring to elements, components, or steps in a non-exclusive manner, indicating that the r eferenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C ....and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0076] While the foregoing describes various embodiments of the
invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE INVENTION
[0077] The proposed invention provides an apparatus that detects a variety
of pulmonary diseases through the analysis of X-Ray images.
[0078] The proposed invention provides a handheld apparatus for
detection of pulmonary diseases through the analysis of X-Ray images by
applying conventional machine learning and deep learning (DL) approaches.
[0079] The proposed invention provides an apparatus that detects
pulmonary diseases through the analysis of X-Ray images automatically, which is
efficient.
[0080] The proposed invention provides an apparatus that detects
pulmonary diseases through the analysis of X-Ray images automatically, which is
easy to use.
[0081] The proposed invention provides an apparatus that obtain X-ray
images of chest of a user, and analyses the X-ray images to find disease
automatically.

[0082] The proposed invention provides an automatic handheld device
that performs diagnosis task accurately.
[0083] The proposed invention provides an apparatus which is used at
hospitals for regular monitoring of patients and timely medication.
[0084] The proposed invention provides an apparatus which gives 24x7
connectivity with government authorities and hospitals for making more effective
availability of medical facilities in remote area.

We Claim:

1. An apparatus to detect medical abnormalities using X-ray images, the
apparatus 100 comprising:
an input unit 102 configured to receive the X-ray images as input, and correspondingly generate a first set of signals;
a processing unit 110 operatively configured with the input unit 102, the processing unit 110 comprising one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors configured to:
receive, the first set of signals;
extract, a plurality of health attributes from the received
first set of signals;
compare, the extracted plurality of health attributes with a
set of pre-defined threshold range to determine medical
abnormalities in body of a user using deep learning algorithms; and generate and transmit a second set of signals to one or more
mobile computing devices 204 using a communication unit 202,
wherein the second set of signals pertains to information relating to
plurality of health attributes and the medical abnormalities found in
the user;
the processing unit further configured to transmit a third set of signals to a server 208, wherein the server 208 is configured to store information associated with the plurality of users, and enable authorised entities 210 to access information using the associated mobile computing device 204.
2. The apparatus as claimed in claim 1, wherein the input unit 102 includes
any or a combination of an X-ray machine to capture X-ray images of
chest of a user in real-time, one or more input ports 118 to receive input
from external devices, and an image capturing unit 104 to capture images
of physical X-ray placed on a pre-defined position.

3. The apparatus as claimed in claim 1, wherein the plurality of health attributes comprises any or a combination of breathing, lung infection, cough, bone structure, and heart shape.
4. The apparatus as claimed in claim 1, wherein said medical abnormalities comprises any or a combination of chronic obstructive pulmonary disease, pulmonary fibrosis, pneumonia, COVID, asthma, lung disease and lung cancer.
5. The apparatus as claimed in claim 1, wherein the communication unit 202 comprises any or combination of Wireless Fidelity (Wi-Fi) Module, Bluetooth Module, Li-Fi Module, Wireless Local Area Network (WLAN), and ZigBee.
6. The apparatus as claimed in claim 1, wherein the deep learning algorithms are selected from a group consisting of support vector machines, decision trees, artificial neural networks, and convolutional neural networks.
7. The apparatus 100 as claimed in claim 1, wherein the apparatus 100 is a handheld apparatus.
8. The apparatus 100 as claimed in claiml, wherein a display unit 124 is operatively coupled with the processing unit 110 to display information relating to plurality of health attributes and the medical abnormalities found in the user, wherein the display unit is selected from a group consisting of, light emitting diode (LED), liquid crystal display (LCD), organic light emitting diode (OLED), and LED matrix.
9. A method 400 to detect medical abnormalities using X-ray images, the method comprising:
receiving, by an input unit, X-ray images of a user;
extracting, by one or more processors, a plurality of health attributes from the received X-ray images;
determining, by the one or more processors, medical abnormalities in the user by analysing the extracted plurality of health attributes.

displaying, by the one or more processors, the medical abnormalities found in the user on a display unit and one or more mobile computing devices; and
storing, at a server, the plurality of health attributes and the medical abnormalities of the user, and enabling authorized entities to access stored information, using the associated mobile computing devices.