Automated Image Feature Extraction and Classification System Using Reinforcement Learning in Medical Imaging

Abstract

This invention presents a reinforcement learning-based system for automated image feature extraction and classification in medical imaging. The system includes an image preprocessing module to enhance image quality, a reinforcement learning-based feature extraction module that autonomously identifies and extracts relevant features, and a classification module that categorizes these features into diagnostic conditions. The system’s reinforcement learning agent uses a reward function to optimize feature extraction, focusing on medically significant features such as tumors and lesions. The system adapts to various imaging modalities and provides labeled, classified outputs with confidence scores for clinical diagnostics, reducing the need for manual feature extraction and supporting accurate, real-time medical decision-making. Accompanied Drawing [FIG. 1]

Specification

Description:[001] The present invention relates to the fields of medical imaging, artificial intelligence, and machine learning, particularly to a system and method using reinforcement learning for automated image feature extraction and classification in medical imaging. This invention is applicable in areas such as radiology, pathology, and oncology, where precise and automated detection, extraction, and classification of image features are essential for accurate diagnostics and clinical decision-making.
BACKGROUND OF THE INVENTION
[002] The following description provides the 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.
[003] Medical imaging technologies, such as MRI, CT, ultrasound, and X-rays, produce complex images that contain critical information necessary for diagnosing various health conditions. Extracting and classifying relevant features from these images, such as tumors, lesions, or organ structures, is a time-consuming and challenging task. Traditional machine learning techniques rely heavily on labeled data and require extensive manual feature engineering, which is often labor-intensive and prone to inconsistencies.
[004]Reinforcement learning (RL) provides a promising approach to automate feature extraction and classification. Unlike traditional supervised learning, RL agents learn by interacting with an environment, receiving rewards for actions that lead to the desired outcome. By applying RL to medical imaging, an automated system can be trained to explore medical images, identify significant features, and classify them accurately based on learned rewards. This approach not only improves the accuracy and efficiency of medical diagnostics but also reduces the need for extensive manual labeling.
[005]This invention introduces a reinforcement learning-based system that autonomously extracts and classifies image features in real time, with minimal human intervention. This system is designed to operate on medical imaging datasets and adapt to various imaging modalities, enhancing the accuracy and speed of diagnostics.
[006] Accordingly, to overcome the prior art limitations based on aforesaid facts. The present invention provides an Automated Image Feature Extraction and Classification System Using Reinforcement Learning in Medical Imaging. Therefore, it would be useful and desirable to have a system, method and apparatus to meet the above-mentioned needs.

SUMMARY OF THE PRESENT INVENTION
[007] This invention provides a reinforcement learning-based system for automated image feature extraction and classification in medical imaging. The system consists of a trained RL agent that interacts with medical images, using a reward-driven approach to extract relevant features and classify them accurately. The system operates through three main modules: (1) an image preprocessing module that enhances image quality and consistency, (2) a reinforcement learning-based feature extraction module that autonomously identifies and extracts significant features from images, and (3) a classification module that categorizes extracted features based on predefined medical conditions.
[008] The RL-based feature extraction module trains the agent to explore image regions and identify features that maximize a reward function tailored to medical imaging objectives. The classification module then uses these extracted features to categorize conditions, such as tumors, lesions, or other abnormalities, into diagnostic categories. The system supports a wide range of medical imaging modalities and enables rapid and accurate diagnostics, reducing the workload for radiologists and clinicians.
[009] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[010] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[011] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
FIG. 1: Block diagram of the reinforcement learning-based image feature extraction and classification system architecture.
FIG. 2: Flowchart of the image preprocessing module, detailing steps to enhance image quality for RL-based analysis.
FIG. 3: Diagram of the reinforcement learning-based feature extraction module, illustrating the interaction of the RL agent with medical images.
FIG. 4: Flowchart of the classification module, showing the process of categorizing extracted features into diagnostic categories.
DETAILED DESCRIPTION OF THE INVENTION
[012] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, 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). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[013] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[014] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
System Architecture (FIG. 1)
[015] The system architecture comprises three main modules: image preprocessing, reinforcement learning-based feature extraction, and classification. These modules work together to automate feature extraction and classification tasks in medical images, providing real-time assistance in medical diagnostics.
[016] Image Preprocessing Module (FIG. 2): The image preprocessing module prepares medical images for analysis by enhancing contrast, normalizing pixel intensity, and applying noise reduction techniques. These preprocessing steps standardize image quality, improving the performance of the RL agent in the feature extraction module. The module also applies image transformations, such as resizing and normalization, to ensure that input data is consistent across different imaging modalities, including MRI, CT, and X-ray.
[017] Reinforcement Learning-Based Feature Extraction Module (FIG. 3): This module employs a reinforcement learning agent trained to identify and extract relevant features from medical images. The agent explores image regions, assessing pixel clusters and structural patterns to locate areas of interest based on a reward function.
[018] The reward function is designed to encourage the agent to focus on medically significant features, such as lesions, tumors, and abnormal tissues, which are associated with specific diagnostic outcomes.
[019] The RL agent receives a positive reward for correctly identifying these features and a negative reward for irrelevant or redundant actions, gradually learning to optimize its feature extraction process. This RL-driven approach allows the system to generalize across different types of medical images, adapting to varied anatomical structures and pathological conditions.
[020] Classification Module (FIG. 4): Once significant features are extracted, the classification module categorizes these features based on known diagnostic conditions. This module utilizes machine learning models, such as convolutional neural networks (CNNs) or support vector machines (SVMs), to classify the extracted features.
[021] The classification module categorizes features into diagnostic categories, such as benign or malignant tumors, healthy or diseased tissue, and other relevant medical conditions. This classification process aids clinicians by providing a preliminary diagnosis, which can be reviewed and verified.
[022] Real-Time Adaptability and Feedback: The RL agent continuously refines its feature extraction strategy based on feedback from the classification module, which enables it to adapt to new imaging datasets and improve accuracy over time. This adaptability is essential for real-time applications in medical imaging, where data quality and clinical needs vary.
[023] Output and Clinical Use: The final output is a medical image with identified features labeled and classified, providing visual markers and confidence scores for each recognized condition. This output supports medical professionals in making informed diagnostic decisions and enhances workflow efficiency by automating preliminary analyses.
Workflow
[024] Image Acquisition and Preprocessing: Medical images are acquired from imaging devices, such as MRI scanners or CT machines, and undergo preprocessing to standardize quality. Contrast adjustment, noise reduction, and normalization are applied to ensure optimal input for feature extraction.
[025] Reinforcement Learning-Based Feature Extraction Process: The preprocessed image is processed by the RL-based feature extraction module. The agent interacts with the image, exploring regions to identify clusters, edges, and textures associated with significant features.
[026] The reward function incentivizes the agent to focus on medically relevant areas, enabling accurate and efficient extraction of diagnostic features.
[027] Feature Classification and Labeling: Extracted features are passed to the classification module, which categorizes them based on diagnostic conditions. Each identified feature is labeled, and a confidence score is assigned, reflecting the likelihood of the diagnosis.
[027] Real-Time Output Generation: The final output, consisting of a classified and labeled image, is displayed for clinical review. This output includes visual markers and diagnostic suggestions, supporting medical professionals in assessing patient conditions promptly.
[028] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[029] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[030] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention. 
, Claims:1. A reinforcement learning-based system for automated image feature extraction and classification in medical imaging, comprising an image preprocessing module, a reinforcement learning-based feature extraction module, and a classification module.
2. The system of claim 1, wherein the image preprocessing module adjusts contrast, normalizes pixel intensity, and reduces noise to enhance image quality for reinforcement learning-based feature extraction.
3. The system of claim 1, wherein the reinforcement learning-based feature extraction module employs a reinforcement learning agent that explores medical images to identify and extract significant features based on a reward function.
4. The system of claim 3, wherein the reward function is designed to maximize extraction of medically relevant features, including lesions, tumors, and abnormal tissues, by providing positive rewards for accurate feature identification.
5. The system of claim 1, wherein the classification module categorizes extracted features into diagnostic categories based on predefined medical conditions using machine learning models.
6. The system of claim 1, wherein the reinforcement learning-based feature extraction module is adaptive to multiple imaging modalities, including MRI, CT, X-ray, and ultrasound.
7. The system of claim 1, further comprising a feedback loop between the classification module and the reinforcement learning agent to improve feature extraction accuracy over time.
8. The system of claim 1, wherein the final output is a medical image with labeled features and associated diagnostic classifications, providing visual markers and confidence scores for clinical review.

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