Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a system and method to detect a cancer and particularly to a system and method for detecting a lung cancer using machine learning techniques.
Description of Related Art
[002] Lung cancer is a malignant tumor that originates in the cells of the lungs, typically in the cells lining air passages. It is a serious health condition characterized by uncontrolled cell growth in the lungs, often leading to breathing difficulties, persistent cough, chest pain, and other severe symptoms. Early detection and accurate diagnosis of lung cancer are crucial for effective treatment and improved patient outcomes.
[003] Machine learning is a branch of artificial intelligence that focuses on the development of algorithms and models enable computers to learn and make predictions or decisions without being explicitly programmed. It involves training computer systems on data and patterns to improve their performance on a specific task or problem. In the context of medical applications, machine learning can be utilized to analyze vast amounts of medical data and assist in diagnosing diseases, predicting outcomes, and optimizing treatment plans.
[004] Further, machine learning can significantly aid in detecting various medical conditions, including lung cancer and lung cancer. By leveraging machine learning algorithms, it is possible to process complex medical data such as imaging scans (e.g., X-rays, CT scans) and clinical information to identify patterns and markers associated with these conditions. Machine learning models can learn from historical data, allowing for the development of accurate predictive models that enhance early detection, diagnosis, and treatment planning.
[005] Despite the aforementioned technical developments, there is still a pressing need for a robust computer-assisted analysis system that can predict lung cancer with the utmost accuracy. Current advancements in machine learning have shown promise in enhancing accuracy and efficiency, but further improvements are essential to develop a highly reliable system for predicting and diagnosing lung cancer, ultimately leading to better patient care and outcomes.
[006] There is thus a need for a system and method for detecting the lung cancer in a more efficient manner.
SUMMARY
[007] Embodiments in accordance with the present invention provide A lung cancer detection system for detecting lung cancer. The system comprising: a processor located on an application server. The system further comprising: a storage medium comprising programming instructions executable by the processor. The storage medium comprises an image receiving module configured to receive medical images from a user device. The storage medium further comprises an image processing module configured to process the received medical images to remove a noise and/or to improve image features of the medical images. The storage medium further comprises a feature extraction module configured to extract the image features based on training images. The storage medium further comprises a classification module configured to classify the received medical images based on the extracted image features using a machine learning algorithm. The storage medium further comprises: a detection module configured to detect a stage of cancer by correlating the classified image features with a pre-stored dataset of stages of cancer.
[008] Embodiments in accordance with the present invention further provide a computer-implemented method for detecting lung cancer. The method includes: receiving medical images from a user device; processing the received medical images to remove a noise and/or to improve image features of the medical images; extracting the image features based on training images; classifying the received medical images based on the extracted image features using a machine learning algorithm; and detecting a stage of cancer by correlating the classified image features with a pre-stored dataset of stages of cancer.
[009] Embodiments of the present invention may provide a number of advantages depending on its particular configuration. First, embodiments of the present application may provide a system and a method for detecting lung cancer.
[0010] Next, embodiments of the present application may provide a lung cancer detection system that may incorporate an efficient machine learning model to predict whether a person is suffering from lung cancer or not based on medical images.
[0011] Next, embodiments of the present application may provide a lung cancer detection system that incorporates a computer-assisted image recognition method for detecting lung cancer.
[0012] Next, embodiments of the present application may provide a lung cancer detection system that detects a stage of cancer by using a machine learning technique.
[0013] These and other advantages will be apparent from the present application of the embodiments described herein.
[0014] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0016] FIG. 1A illustrates a block diagram depicting a lung cancer detection system, according to an embodiment of the present invention;
[0017] FIG. 1B illustrates a storage medium of the lung cancer detection system, according to an embodiment of the present invention;
[0018] FIG. 1C illustrates medical images for the lung cancer detection system, according to an embodiment of the present invention; and
[0019] FIG. 2 depicts a flowchart of a method for detecting lung cancer by the lung cancer detection system, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1A illustrates a block diagram depicting a lung cancer detection system 100 (hereinafter referred to as the system 100), according to an embodiment of the present invention. The system 100 may be configured to detect lung cancer by employing a computer-assisted image recognition technique. In an embodiment of the present invention, the system 100 may further detect a stage of the lung cancer by analyzing medical images using a machine learning technique. According to embodiments of the present invention, the medical images may include radiological images such as, but not limited to, X-radiation (X-RAY) images, Computed Tomography (CT) scan images, Magnetic Resonance (MR) images, Positron Emission Tomography (PET) images, Single Photon Emission Computed Tomography (SPECT) images, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the medical images including known related art and/or later developed technologies.
[0025] According to an embodiment of the present invention, the system 100 may comprise an application server 102, a user device 104, a computer application 106, a communication network 108, a processor 110, and a storage medium 112.
[0026] In an embodiment of the present invention, the application server 102 may be, but not limited to, a laptop, a desktop, and alike. The application server 102 may be a cloud server, in an embodiment of the present invention. Embodiments of the present invention are intended to include or otherwise cover any type of the application server 102 including known, related art, and/or later developed technologies.
[0027] Further, the user device 104 may be a device used by a user to provide an input to the system 100. The user device 104 may further be used by the user to receive a notification related to the detection of the infection as recognized by the system 100, in an embodiment of the present invention. The user device 104 may be, but not limited to, a personal computer, a consumer device, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 104 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the personal computer may be, but not limited to, a desktop, a server, a laptop, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the personal computer including known, related art, and/or later developed technologies.
[0028] Further, in an embodiment of the present invention, the consumer device may be, but not limited to, a tablet, a mobile phone, a notebook, a netbook, a smartphone, a wearable device, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the consumer device including known, related art, and/or later developed technologies. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 104 including known, related art, and/or later developed technologies.
[0029] In a preferred embodiment of the present invention, the user device 104 may comprise the computer application 106 that may be a computer-readable program installed on the user device 104 for executing functions associated with the system 100. Further, in an embodiment of the present invention, the user may login into the system 100 through the computer application 106 by providing login details such as, but not limited to, a user identifier, a password, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the login details that may be associated with the user. Upon login into the system 100, the user may input the medical images into the system 100 by using the computer application 106. In another embodiment of the present invention, the user may login into the system 100 through a web browser by providing the login details. In such embodiment of the present invention, the user may input the medical images into the system 100 through the web browser upon login into the system 100. In yet another embodiment of the present invention, the user may input the medical images into the system 100 by using the computer application 106 without providing the login details into the system 100.
[0030] In an embodiment of the present invention, the communication network 108 may enable a communication between the application server 102 and the user device 104 such that the user may input the medical images to the system 100. According to an embodiment of the present invention, the communication network 108 may be a data network such as, but not limited to, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the data network, including known, related art, and/or later developed technologies. In another embodiment of the present invention, the communication network 108 may be a wireless network, such as, but not limited to, a cellular network and may employ various technologies including an Enhanced Data Rates for Global Evolution (EDGE), a General Packet Radio Service (GPRS), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the wireless network, including known, related art, and/or later developed technologies. According to an embodiment of the present invention, the application server 102 and the user device 104 may be configured to communicate with each other by communication mediums (not shown) connected to the communication network 108. The communication mediums may be for example, but not limited to, a coaxial cable, a copper wire, a fiber optic, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the communication mediums, including known, related art, and/or later developed technologies.
[0031] In an embodiment of the present invention, the processor 110 may be located on the application server 102. The processor 110 may be configured to execute programming instructions associated with the system 100, in an embodiment of the present invention. According to embodiments of the present invention, the processor 110 may be, but not limited to, a Programmable Logic Control unit (PLC), a microcontroller, a microprocessor, a computing device, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processor 110 including known, related art, and/or later developed technologies.
[0032] In an embodiment of the present invention, the storage medium 112 may comprise the programming instructions. In an embodiment of the present invention, the programming instructions may be a cancer detection algorithm that may be executed by the processor 110 to detect the lung cancer. In another embodiment of the present invention, the programming instructions may be a stage detection algorithm that may be executed by the processor 110 to detect the stage of the lung cancer. In a preferred embodiment of the present invention, the cancer detection algorithm may be a deep learning algorithm that may be capable of providing accurate results for detecting the lung cancer.
[0033] The storage medium 112 may be a non-transitory storage medium that may be configured to store the programming instructions for controlling operations of the system 100, according to an embodiment of the present invention. The storage medium 112 may be, but not limited to, a Random-Access Memory device, a Read-Only Memory Device, a flash memory, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the storage medium 112 including known, related art, and/or later developed technologies.
[0034] FIG. 1B illustrates the storage medium 112 of the system 100, according to an embodiment of the present invention. In an embodiment of the present invention, the storage medium 112 may comprise non-limiting programming modules such as an image receiving module 114, an image processing module 116, a feature extraction module 118, a classification module 120, a detection module 122, and a recommendation module 124.
[0035] In an embodiment of the present invention, the image receiving module 114 may be configured to receive the medical images from the user device 104. The image receiving module 114 may store the medical images in an associated memory (not shown). In an embodiment of the present invention, the image receiving module 114 may temporarily store the medical images in the associated memory. In another embodiment of the present invention, the image receiving module 114 may permanently store the medical images in the associated memory. In an embodiment of the present invention, the image receiving module 114 may receive the medical images in a pre-defined format. According to embodiments of the present invention, the format of the medical images may be a Tagged Image File Format (TIFF), a Joint Photographic Expert Group (JPEG), a Graphics Interchange Format (GIF), a Portable Network Graphics (PNG), Bitmap, an Encapsulated PostScript (EPS), RAW Image Files, and so forth. In other embodiments of the present invention, the format of the medical images may be a word format, a portable document format (pdf), and any other format, now known, or later developed in technologies.
[0036] In an embodiment of the present invention, the image processing module 116 may be configured to process the received medical images to enhance image features of the received medical images. In an embodiment of the present invention, the image processing module 116 may remove a noise and/or improve image features of the medical images. In another embodiment of the present invention, the image processing module 116 may remove background details from the medical images. In a further embodiment of the present invention, the image processing module 116 may reconstruct the medical images. The image processing module 116 may further be configured to perform various manipulation functions on the received medical images based on query string parameters. The query string parameters may be pre-stored parameters that may be, but not limited to, a CachedImagePath, an OnPostProcessing, an eventargs, a security watermark, an alpha transparency, a RawUrl, and so forth.
[0037] In an embodiment of the present invention, the image processing module 116 may enhance the medical images by removing a noise and improving a contrast by applying, referring but not limited to, a median filter (not shown), an average filter (not shown), a histogram equalization, and so forth. In an embodiment of the present invention, the feature extraction module 118 may extract the image features of the medical images. The feature extraction module 118 may employ a Histogram of Oriented Gradient (HOG) technique to extract the image features from the medical images, in accordance with an embodiment of the present invention. According to embodiments of the present invention, the feature extraction module 118 may employ any other technique for the extraction of the image features.
[0038] In an embodiment of the present invention, the feature extraction module 118 may correlate the medical images with training images to extract the image features. The training images may comprise X-radiation (X-RAY) images, Computed Tomography (CT) scan images, Magnetic Resonance (MR) images, Positron Emission Tomography (PET) images, Single Photon Emission Computed Tomography (SPECT) images of infected, uninfected, less infected and highly infected people, according to an embodiment of the present invention. In an embodiment of the present invention, after extraction, the features may be separated into a positive region and a negative region. The positive region and the negative region may indicate the image features related to a possibility of infection and a possibility of no symptoms, respectively.
[0039] In an embodiment of the present invention, the classification module 120 may be configured to classify the extracted features as obtained from the feature extraction module 118. In an embodiment of the present invention, the classification module 120 may incorporate the machine learning algorithms such as, but not limited to, a convolutional neural network (CNN) to perform the classification of the extracted image features.
[0040] The classification module 120 may be configured to infuse the image features into a confusion matrix. In an embodiment of the present invention, the detection module 122 may be configured to detect the lung cancer based on an outcome of the confusion matrix. To solve the confusion matrix, four statistical indices may be used such as, a true positive (TP), a true negative (TN), a false positive (FP), a false negative (FN). In an embodiment of the present invention, the detection module 122 may detect the stage of cancer by correlating the classified image features with a pre-stored dataset of stages of cancer. In an embodiment of the present invention, the detection module 122 may detect the stage of cancer such that a zero stage of cancer, a first stage of cancer, a second stage of cancer, a third stage of cancer, a fourth stage of cancer, and so forth. In an embodiment of the present invention, the zero stage of cancer may be evaluated when there is no symptoms detected in the medical images provided to the system 100. In an embodiment of the present invention, the first stage of cancer may be a low stage of the lung cancer. The second stage of cancer may be a moderate stage of the lung cancer. The third stage of cancer may be a high stage of the lung cancer. The fourth stage of cancer may be a very high stage of the lung cancer.
[0041] In an embodiment of the present invention, the recommendation module 124 may be configured to recommend precautions and remedies based on the detected stage of the lung cancer. The recommendation module 124 may provide recommendations that may be, but not limited to, home remedies, expert recommendations, test recommendations, aided devices, lab details, motivational videos, fitness videos, weblinks for third-party products and services, and so forth. In an embodiment of the present invention, the recommendations may be displayed on the user device 104 (as shown in the FIG. 1A).
[0042] FIG. 1C illustrates the medical images for the lung cancer detection system, according to an embodiment of the present invention. In an embodiment of the present invention, the medical images may be images of a patient’s chest that may be inflamed with the lung cancer. In an embodiment of the present invention, the medical images of the patient may be uploaded to the system 100 for detecting the lung cancer and/or the stage of the lung cancer. The system 100 may be trained with the training images for the detection of the lung cancer. The training images may comprise the medical images of chest and/or lungs of the uninfected patient, in accordance with an embodiment of the present invention. In another embodiment of the present invention, the training images may comprise the medical images of the chest and/or the lungs of the moderately and/or a highly infected patient.
[0043] FIG. 2 depicts a flowchart of a method 200 for detecting the lung cancer by the system 100, according to an embodiment of the present invention.
[0044] At step 202, the system 100 may receive the medical images from the user device 104.
[0045] At step 204, the system 100 may process the received medical images for enhancing the image features of the received medical images.
[0046] At step 206, the system 100 may extract the image features of the received medical images.
[0047] At step 208, the system 100 may classify the received medical images based on the extracted image features using the machine learning algorithm.
[0048] At step 210, the system 100 may detect the stage of cancer by correlating the classified image features with the pre-stored dataset of stages of cancer.
[0049] Embodiments of the invention are described above with reference to block diagrams and schematic illustrations of methods and systems according to embodiments of the invention. It will be understood that each block of the diagrams and combinations of blocks in the diagrams can be implemented by computer program instructions. These computer program instructions may be loaded onto one or more general purpose computers, special purpose computers, or other programmable data processing apparatus to produce machines, such that the instructions which execute on the computers or other programmable data processing apparatus create means for implementing the functions specified in the block or blocks. Such computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the block or blocks.
[001] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
[002] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A lung cancer detection system (100) for detecting lung cancer, the system (100) comprising:
a processor (110) located on an application server (102); and
a storage medium (112) comprising programming instructions executable by the processor (110), wherein the storage medium (112) comprises:
an image receiving module (114) configured to receive medical images from a user device (104);
an image processing module (116) configured to process the received medical images to remove a noise and/or to improve image features of the medical images;
a feature extraction module (118) configured to extract the image features based on training images;
a classification module (120) configured to classify the received medical images based on the extracted image features using a machine learning algorithm; and
a detection module (122) configured to detect a stage of cancer by correlating the classified image features with a pre-stored dataset of stages of cancer.
2. The system (100) as claimed in claim 1, wherein the medical images are selected from X-radiation (X-RAY) images, Computed Tomography (CT) scan images, Magnetic Resonance (MR) images, Positron Emission Tomography (PET) images, Single Photon Emission Computed Tomography (SPECT) images, or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the machine learning algorithm is selected from a convolutional neural network (CNN).
4. The system (100) as claimed in claim 1, wherein the training images comprises X-radiation (X-RAY) images, Computed Tomography (CT) scan images, Magnetic Resonance (MR) images, Positron Emission Tomography (PET) images, Single Photon Emission Computed Tomography (SPECT) images, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the stage of cancer is selected from a zero stage of cancer, a first stage of cancer, a second stage of cancer, a third stage of cancer, and a fourth stage of cancer.
6. The system (100) as claimed in claim 1 further comprising a computer application (106) installable on the user device (104).
7. The system (100) as claimed in claim 1, further comprising a recommendation module (124) for providing precautions and remedies on the user device (104) based on the detected stage of the lung cancer.
8. A method for detecting a lung cancer using a lung cancer detection system (100), the method comprising steps of:
receiving medical images from a user device (104);
processing the received medical images to remove a noise and/or to improve image features of the medical images;
extracting the image features based on training images;
classifying the received medical images based on the extracted image features using a machine learning algorithm; and
detecting a stage of cancer by correlating the classified image features with a pre-stored dataset of stages of cancer.
9. The method as claimed in claim 8, further comprising a step of providing precautions and remedies on the user device (104) based on the detected stage of the lung cancer using a recommendation module (124).
10. The method as claimed in claim 8, wherein the stage of cancer is selected from a zero stage of cancer, a first stage of cancer, a second stage of cancer, a third stage of cancer, and a fourth stage of cancer.

Date: November 30, 2023
Place: Noida

Dr. Keerti Gupta
Agent for the Applicant
(IN/PA-1529)