Description:Title of The Invention
A Medical System for Convolutional Neural Network-Based Automated Diagnosis Images
Field of the Invention
This invention relates to A Medical System for Convolutional Neural Network-Based Automated Diagnosis Images
Background of the Invention
Manual analysis of medical images can be time-consuming, especially when dealing with a large volume of images. Healthcare professionals would need to spend significant time reviewing and interpreting each image individually, which can result in delays in diagnosis and treatment planning. Without a standardized and automated system, the interpretation of medical images becomes subjective and may vary between different healthcare professionals. This subjectivity can lead to inconsistencies and discrepancies in diagnoses, affecting the accuracy and reliability of patient care.
US10796181B2 discloses Methods and systems for addressing malfunction of a medical imaging device are disclosed. The method includes classifying a type of an image artifact in a medical image acquired by the medical imaging device by using a trained machine learning model. The method also includes analyzing system data associated with acquisition of the medical image to identify one or more system parameters that might have contributed to the type of image artifact and providing an action for addressing the image artifact based on the identified one or more system parameters.
Research Gap: CNN-based medical systems are easily scalable to handle a huge number of medical pictures, making them ideal for busy healthcare facilities with high image workloads. The system can analyse many photos at the same time, allowing for more efficient analysis and diagnosis.
EP3848893A1 discloses a method for automatically segmenting three-dimensional blood vessel data from three-dimensional medical image data of a patient through the use of a computer is provided. The method includes: receiving the three-dimensional medical image data of the patient; generating three-dimensional shape machine-learning blood vessel data from the received three-dimensional medical image data through the use of a machine-learned segmentation program so as to generate three-dimensional blood vessel data; and generating corrected three-dimensional shape blood vessel data from the received three-dimensional medical image data and the generated three-dimensional shape machine-learning blood vessel data through the use of an image processing program. The three-dimensional machine-learning blood vessel data is composed of at least one noise data set other than a blood vessel region and a data set in which the blood vessel region is missing. The image processing program is configured to compare the received three-dimensional medical image data and the generated three-dimensional shape machine-learning blood vessel data to match the blood vessel region, supplement the missing data set, and remove the noise data set, so as to generate corrected three-dimensional shape blood vessel data.
Research Gap: Human interpretation of medical images may vary due to factors like fatigue, experience, and subjectivity. In contrast, CNN-based systems provide consistent and standardized results. Once trained on a diverse dataset, the network applies the same set of rules consistently, eliminating the potential for human error or bias.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is A Medical System for Convolutional Neural Network-Based Automated Diagnosis Images
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
An automated diagnosis system for medical images using Convolutional Neural Networks (CNNs) is a powerful application of deep learning in the field of healthcare. CNNs are particularly well-suited for image analysis tasks due to their ability to learn hierarchical representations from the data. This system comprises of controlling unit (101) it oversees the data flow across the system. It manages the collecting of medical pictures from sensors, provides adequate image preprocessing, and allows image input into the CNN for analysis. It might entail developing communication protocols between sensors, preprocessing modules, and the CNN model, X- ray Sensor (102) are routinely employed to take pictures of bones, organs, and other structures to acquire X-ray pictures, X-ray machines employ detectors such as digital flat panel detectors or image intensifiers, Computed Tomography (CT) sensor (103) CT scanners provide comprehensive cross-sectional pictures of the body by combining X-rays and detectors CT scanner detectors collect X-ray attenuation data from numerous angles in order to assemble 3D pictures, Magnetic Resonance Imaging Sensor (104) MRI scanners provide comprehensive pictures of the body's interior structures by using high magnetic fields and radio waves. Sensors in MRI are generally superconducting coils that detect radio frequency signals released by the body's tissues, Positron Emission Tomography (PET) sensor (105) PET scanners detect metabolic activity in tissues by using radioactive tracers. PET scanner sensors are generally arrays of scintillation crystals or solid-state detectors that detect the gamma rays generated by the tracers, Single Photon Emission Computed Tomography (SPECT) Sensor (106) Radioactive tracers are also used by SPECT scanners to produce 3D pictures of tissues. SPECT, like PET, captures released gamma rays using scintillation crystals or solid-state detectors, Digital Radiography (DR) Sensor (107) It uses digital detectors to capture X-ray pictures directly, eliminating the need for film. These detectors can be made of amorphous silicon or amorphous selenium, power supply (108) it supplies the power to the entire system.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1.1 (Automated Diagnosis Images Sensing unit)
The figure 1.2 Consists of Mammography sensor (109) Mammography captures photographs of breast tissue using X-rays.
Figure 1.3 (Controlling unit)
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
An automated diagnosis system for medical images using Convolutional Neural Networks (CNNs) is a powerful application of deep learning in the field of healthcare. CNNs are particularly well-suited for image analysis tasks due to their ability to learn hierarchical representations from the data. This system comprises of controlling unit (101) it oversees the data flow across the system. It manages the collecting of medical pictures from sensors, provides adequate image preprocessing, and allows image input into the CNN for analysis. It might entail developing communication protocols between sensors, preprocessing modules, and the CNN model, X- ray Sensor (102) are routinely employed to take pictures of bones, organs, and other structures to acquire X-ray pictures, X-ray machines employ detectors such as digital flat panel detectors or image intensifiers, Computed Tomography (CT) sensor (103) CT scanners provide comprehensive cross-sectional pictures of the body by combining X-rays and detectors CT scanner detectors collect X-ray attenuation data from numerous angles in order to assemble 3D pictures, Magnetic Resonance Imaging Sensor (104) MRI scanners provide comprehensive pictures of the body's interior structures by using high magnetic fields and radio waves. Sensors in MRI are generally superconducting coils that detect radio frequency signals released by the body's tissues, Positron Emission Tomography (PET) sensor (105) PET scanners detect metabolic activity in tissues by using radioactive tracers. PET scanner sensors are generally arrays of scintillation crystals or solid-state detectors that detect the gamma rays generated by the tracers, Single Photon Emission Computed Tomography (SPECT) Sensor (106) Radioactive tracers are also used by SPECT scanners to produce 3D pictures of tissues. SPECT, like PET, captures released gamma rays using scintillation crystals or solid-state detectors, Digital Radiography (DR) Sensor (107) It uses digital detectors to capture X-ray pictures directly, eliminating the need for film. These detectors can be made of amorphous silicon or amorphous selenium, power supply (108) it supplies the power to the entire system.
The figure 1.2 Consists of Mammography sensor (109) Mammography captures photographs of breast tissue using X-rays. Mammography detectors, which are dedicated sensors, are meant to deliver high-resolution pictures with a minimal radiation exposure. Amorphous selenium or other scintillator technologies are frequently used in these detectors, Fluoroscopy sensor (110) Fluoroscopy is the use of X-rays to see moving organs such as the digestive system or blood arteries in real time. Fluoroscopy sensors are image intensifiers or flat panel detectors that allow continuous X-ray imaging, Optical Coherence Tomography (OCT) sensor (111) OCT is an imaging technology that captures high-resolution cross-sectional pictures of tissues using light waves OCT systems employ fiber optic probes or interferometers to detect backscattered light,
Endoscope Sensor (112) Endoscopy involves inserting a thin, flexible tube with a camera into the body to visualize internal organs or cavities. The sensors used in endoscopy systems are typically small cameras or imaging sensors at the tip of the endoscope, Confocal Microscopy Sensor (113) Confocal microscopy is a high-resolution imaging method that gives pictures of cellular components in great detail. To reject out-of-focus light, it employs a laser light source and pinhole apertures. Photomultiplier tubes or photodiode arrays are commonly used as sensors in confocal microscopy systems, Thermal Imaging Sensor (114) it is also known as infrared imaging, captures the heat emitted by the body to visualize temperature variations. Thermal imaging sensors are typically based on microbolometers or thermopile arrays, power supply (115) it supplies the power to the entire system.
In figure 1.3 the data of figure 1.1 and data of Figure 1.2 sends to controlling unit (101) Preprocessing of medical images is often necessary to enhance the quality of the data and prepare it for input into the CNN. The controlling unit coordinates the execution of preprocessing steps, such as resizing, normalization, noise reduction, and image augmentation techniques. It ensures that the appropriate preprocessing operations are applied to the images before feeding them into the CNN mode, then all the collected information controlling unit (101) sends to cloud server (116) via internet and there is patients database which contain the data and this data can only be viewed by the hospital/doctor, LoRa enables long-range communication, allowing sensors to transmit data over distances of several kilometers, depending on the specific environment and conditions. This is particularly useful in healthcare settings where sensors are distributed across a large area.

ADVANTAGES OF THE INVENTION:
• CNNs can learn complicated patterns and characteristics from medical pictures, allowing them to make precise diagnoses. Automated diagnostic systems can give consistent and objective assessments, eliminating human error and interpretation variability.
• Automated systems can process medical images and provide diagnoses in a fraction of the time compared to manual analysis by medical professionals. This expedites the diagnosis process and enables timely treatment planning, leading to improved patient outcomes and reduced waiting times.
• This automating medical image analysis, healthcare practitioners may focus on other vital duties, maximizing their time and productivity. Because automated solutions can manage a high volume of patients, medical personnel may devote their time to more challenging situations or give personalized treatment.
• In locations where access to specialised medical experts is limited, automated diagnostic systems can bridge the gap by offering remote access to expert-level analyses. This can assist to extend medical services to neglected areas and improve healthcare access.
• CNNs can detect tiny patterns or anomalies in medical pictures that human viewers may find difficult to discern. Automated diagnostic systems have the potential to help in illness identification, allowing for prompt treatments and improved prognosis.
• As the automated system encounters new instances and gets expert feedback, it will be able to improve its diagnostic capabilities and adapt to changing medical knowledge.
, Claims:We Claim:
1. A Medical System for Convolutional Neural Network-Based Automated Diagnosis Images comprises of Controlling Unit (101), X-Ray Sensor (102), Computed Tomography (103), Magnetic Resonance Imaging (MRI) Sensors (104), Positron Emission Tomography (PET) Sensors (105), Single-Photon Emission computed Tomography (SPECT) Sensors (106), Digital Radiography (DR) sensors (107), Power Supply (108), Controlling Unit (101), Mammography Sensor (109), Fluoroscopy Sensors (110), Optical Coherence Tomography (OCT) sensors (111), Endoscopy Sensors (112), Confocal microscopy sensors(113), Thermal Imaging Sensors (114), Cloud Server (116), Patient Database (117), Hospital (118).
2. The system as claimed in claim 1, wherein controlling unit (101) oversees the data flow across the system; it manages the collecting of medical pictures from sensors, provides adequate image preprocessing, and allows image input into the CNN for analysis; entails developing communication protocols between sensors, preprocessing modules, and the CNN model, X- ray Sensor (102) are routinely employed to take pictures of bones, organs, and other structures to acquire X-ray pictures, X-ray machines employ detectors such as digital flat panel detectors or image intensifiers, Computed Tomography (CT) sensor (103) CT scanners provide comprehensive cross-sectional pictures of the body by combining X-rays and detectors CT scanner detectors collect X-ray attenuation data from numerous angles in order to assemble 3D pictures, Magnetic Resonance Imaging Sensor (104) MRI scanners provide comprehensive pictures of the body's interior structures by using high magnetic fields and radio waves.
3. The system as claimed in claim 1, wherein Sensors in MRI are superconducting coils that detect radio frequency signals released by the body's tissues, Positron Emission Tomography (PET) sensor (105) PET scanners detect metabolic activity in tissues by using radioactive tracers; wherein PET scanner sensors are generally arraying of scintillation crystals or solid-state detectors that detect the gamma rays generated by the tracers, Single Photon Emission Computed Tomography (SPECT) Sensor (106) Radioactive tracers are also used by SPECT scanners to produce 3D pictures of tissues. SPECT, like PET, captures released gamma rays using scintillation crystals or solid-state detectors, Digital Radiography (DR) Sensor (107) It uses digital detectors to capture X-ray pictures directly, eliminating the need for film; and these detectors are made of amorphous silicon or amorphous selenium, power supply (108) it supplies the power to the entire system.
4. The system as claimed in claim 1, whereinMammography sensor (109) Mammography captures photographs of breast tissue using X-rays; and Mammography detectors, which are dedicated sensors, are meant to deliver high-resolution pictures with a minimal radiation exposure.
5. The system as claimed in claim 1, wherein Amorphous selenium or other scintillator technologies are frequently used in these detectors, Fluoroscopy sensor (110) Fluoroscopy is the use of X-rays to see moving organs such as the digestive system or blood arteries in real time; wherein Fluoroscopy sensors are image intensifiers or flat panel detectors that allow continuous X-ray imaging, Optical Coherence Tomography (OCT) sensor (111) OCT is an imaging technology that captures high-resolution cross-sectional pictures of tissues using light waves OCT systems employ fiber optic probes or interferometers to detect backscattered light.
6. The system as claimed in claim 1, whereinEndoscope Sensor (112) Endoscopy involves inserting a thin, flexible tube with a camera into the body to visualize internal organs or cavities; said sensors used in endoscopy systems are typically small cameras or imaging sensors at the tip of the endoscope, Confocal Microscopy Sensor (113) Confocal microscopy is a high-resolution imaging method that gives pictures of cellular components in great detail; and to reject out-of-focus light, it employs a laser light source and pinhole apertures. Photomultiplier tubes or photodiode arrays are commonly used as sensors in confocal microscopy systems, Thermal Imaging Sensor (114) it is also known as infrared imaging, captures the heat emitted by the body to visualize temperature variations; and thermal imaging sensors are typically based on microbolometers or thermopile arrays, power supply (115) it supplies the power to the entire system.
7. The system as claimed in claim 1, whereincontrolling unit (101) Preprocessing of medical images enhances the quality of the data and prepare it for input into the CNN; and the controlling unit coordinates the execution of preprocessing steps, such as resizing, normalization, noise reduction, and image augmentation techniques. It ensures that the appropriate preprocessing operations are applied to the images before feeding them into the CNN mode, then all the collected information controlling unit (101) sends to cloud server (116) via internet and there is patients’ database which contain the data and this data only be viewed by the hospital/doctor, LoRa enables long-range communication, allowing sensors to transmit data over distances of several kilometers, depending on the specific environment and conditions.
8. The system as claimed in claim 1, wherein automated solutions based on CNN give quantitative assessments and objective metrics for many parameters inside medical pictures; which helps track illness development, evaluate therapy efficacy, and facilitate longitudinal investigations.