MACHINE LEARNING-BASED IMAGE RECOGNITION FOR HEALTHCARE DIAGNOSTICS

Abstract

The present invention relates to a machine learning-based system for the automated recognition, classification, and diagnosis of medical conditions from healthcare imaging data. By utilizing advanced deep learning techniques, particularly convolutional neural networks (CNNs), the system processes medical images such as X-rays, CT scans, MRIs, and ultrasound images to identify and classify various medical conditions, including tumors, fractures, and infections. The system provides diagnostic results with confidence scores, supporting healthcare professionals in making informed decisions. It integrates seamlessly into clinical workflows, offering real-time analysis, user-friendly interfaces, and continuous learning capabilities, ultimately improving diagnostic accuracy and efficiency while reducing human error in image interpretation.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, 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. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention provides a machine learning-based system for automating the analysis and diagnosis of medical images. This system uses advanced deep learning techniques to recognize, classify, and diagnose a wide range of medical conditions by processing healthcare imaging data. Below is a detailed description of the system's components, working mechanism, and functionality.

The system receives medical images from various imaging devices such as X-rays, CT scans, MRIs, and ultrasound machines. These images are first processed by an image preprocessing module, which serves to enhance the quality and prepare the images for further analysis. The preprocessing step may involve normalizing pixel values, reducing noise, and performing other image enhancement techniques to improve clarity and uniformity.

Once the images are preprocessed, the system uses a feature extraction module to identify key characteristics within the images. This module leverages advanced image processing algorithms and machine learning techniques to extract relevant features, such as shape, texture, size, and pattern of any abnormalities present in the image. These features are critical for the subsequent classification step.

The heart of the system is a deep learning model, particularly a convolutional neural network (CNN). The CNN is trained on a vast dataset of labeled medical images to recognize patterns that indicate specific medical conditions such as tumors, fractures, or infections. The model learns to map the extracted features to diagnostic categories based on previously labeled data, allowing it to identify anomalies and classify the condition of the patient.

The training phase involves feeding the system with large amounts of labeled data, allowing the machine learning algorithm to identify underlying features associated with specific conditions. A validation process ensures that the model is accurately identifying these patterns without overfitting. The model continuously learns and adapts to improve accuracy as more data becomes available.

After the model analyzes the extracted features, it classifies the medical image into one of several predefined categories. These categories may include "normal," "benign," "malignant," "fracture," "infection," or any other relevant diagnostic classification. The system then outputs a diagnostic result, which includes not only the classification but also a confidence score indicating the system's certainty about the diagnosis.

The system is designed with a user-friendly interface that provides healthcare professionals with a clear display of the diagnostic results. This interface can overlay the system's findings on the original medical images, highlighting areas of interest such as tumors or fractures, and showing the confidence score. Clinicians can interact with the interface, review the images, and make informed decisions based on the system's recommendations.

The system is equipped with a feedback mechanism that enables continuous learning. Healthcare professionals can provide feedback on the accuracy of the diagnostic results, which is then used to retrain the model periodically. This feedback loop helps improve the performance of the system over time, ensuring that it adapts to new conditions and datasets, increasing its robustness and accuracy.

The system can be seamlessly integrated with existing healthcare infrastructure, such as hospital information systems (HIS) or radiology picture archiving and communication systems (PACS). It can also be deployed as a cloud-based solution, enabling remote access and analysis by clinicians from different locations. Integration with these systems allows for easier sharing of diagnostic results, improving collaboration between healthcare professionals.

The system incorporates robust data privacy and security features to ensure that patient data is protected. All medical images and diagnostic results are encrypted and stored in compliance with healthcare regulations such as HIPAA, ensuring that patient confidentiality is maintained. Access to sensitive data is controlled and monitored to prevent unauthorized access.

In one embodiment, the system is used to automate the detection of tumors in chest X-ray images. The system receives X-ray images from a digital imaging system and preprocesses them by enhancing the contrast and reducing image noise. Using a deep learning model (CNN), the system extracts relevant features such as abnormal growths, irregular masses, and patterns indicative of malignant tumors.

The model classifies the images into categories such as "no tumor," "benign tumor," or "malignant tumor," and provides a confidence score for each diagnosis. The diagnostic results are displayed on a user interface, where healthcare providers can view the X-ray image with highlighted areas that correspond to potential tumors. This embodiment helps radiologists identify tumors early and efficiently, facilitating faster diagnosis and treatment.

In another embodiment, the system is designed for detecting fractures in MRI scans. MRI images of a patient's bones are acquired and processed by the system's preprocessing module to enhance the clarity of bone structures and reduce any distortions or noise. The system then employs a CNN-based machine learning model to analyze the MRI images and extract features such as cracks, irregularities in bone structure, or displaced bone fragments.

The model classifies the images into categories such as "normal bone structure," "fracture detected," or "suspected fracture," with an associated confidence score. The results, including the highlighted fractured areas, are displayed on a user-friendly interface for the clinician. This embodiment assists orthopedic specialists by automating the process of fracture detection, reducing human error and ensuring faster diagnosis.
While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for machine learning-based image recognition for healthcare diagnostics, comprising:
receiving a medical image from a medical imaging device;
preprocessing the image to enhance quality and normalize pixel values;
extracting features from the preprocessed image;
applying a machine learning model to the extracted features to classify the image into one or more diagnostic categories;
outputting a diagnostic result indicating the likelihood of a medical condition based on the classification.

2.The method of claim 1, wherein the machine learning model comprises a convolutional neural network (CNN) trained on a labeled dataset of medical images.

3.The method of claim 1, further comprising displaying the diagnostic result on a user interface, including a confidence score associated with the diagnostic result.

4.The method of claim 1, wherein the medical image is selected from the group consisting of X-ray images, CT scan images, MRI images, and ultrasound images.

5.The method of claim 1, wherein the system provides real-time image analysis and diagnostic results to healthcare professionals.

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