Description:[001] The present invention relates to the field of medical imaging and diagnostic technologies. More specifically, it pertains to a system and method for detecting abnormalities in X-ray and computed tomography (CT) scan images using advanced computational techniques to assist in accurate and efficient medical diagnosis. The invention encompasses automated image analysis, anomaly detection, and interpretation to support healthcare professionals in identifying pathological conditions, improving diagnostic accuracy, and enhancing clinical outcomes.
BACKGROUND OF THE INVENTION
[002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[003] Medical imaging plays a vital role in diagnosing and monitoring various diseases by enabling healthcare professionals to visualize internal body structures non-invasively. Among the most commonly used imaging modalities are X-ray and computed tomography (CT) scans, which provide detailed images of bones, tissues, and organs. Despite advancements in imaging technology, accurate and timely detection of abnormalities remains a critical challenge, as it often relies heavily on human interpretation.
[004] Radiologists and medical professionals manually examine X-ray and CT images to identify abnormal features such as tumors, fractures, or infections. This manual process is prone to errors due to factors such as human fatigue, varying levels of expertise, and large volumes of data requiring analysis. Hence, there is a growing need for automated systems to assist in image interpretation and improve diagnostic accuracy.
[005] Several prior art systems have attempted to address this challenge by leveraging computer-aided diagnostic (CAD) technologies. For instance, systems employing basic image processing techniques have been developed to highlight regions of interest in X-ray images. Additionally, machine learning models have been applied to classify CT images for specific conditions, such as lung disease detection. Despite these efforts, existing systems often lack the robustness and adaptability required to handle the diversity of medical conditions presented in clinical practice.
[006] Another category of prior art involves rule-based algorithms that segment and detect abnormalities by pre-defined criteria. While these approaches can provide initial guidance, they struggle with complex or ambiguous cases and do not generalize well to images outside their training datasets. Furthermore, many existing solutions require significant manual intervention during setup, calibration, or data labeling phases.
[007] These prior approaches suffer from several limitations. They frequently produce false positives and false negatives, leading to diagnostic errors that can compromise patient care. Many systems are computationally expensive, limiting their deployment in resource-constrained environments such as rural hospitals or smaller clinics. Moreover, lack of integration with existing hospital systems and limited interpretability of results often hinder their widespread adoption by medical professionals.
[008] The present invention overcomes these limitations by providing an advanced system and method for detecting abnormalities in X-ray and CT scan images. By incorporating sophisticated image analysis techniques, artificial intelligence (AI) models, and adaptive learning algorithms, the invention ensures high accuracy, adaptability, and scalability. The system reduces false detection rates, requires minimal manual intervention, and seamlessly integrates with hospital information systems, thereby enhancing diagnostic capabilities and improving patient outcomes.
SUMMARY OF THE INVENTION
[009] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[010] The present invention provides a system and method for detecting abnormalities in X-ray and CT scan images using advanced image processing and artificial intelligence (AI) techniques. The system comprises an image acquisition module, a pre-processing unit for enhancing image quality, an AI-based detection module for identifying abnormal regions, and an output interface for visualizing results and generating diagnostic reports. The method involves acquiring medical images, enhancing contrast, segmenting regions of interest, and applying trained neural networks to detect and classify abnormalities. The system is adaptable to various imaging conditions and offers real-time or near-real-time analysis to assist radiologists in clinical diagnosis.
[011] The invention improves diagnostic accuracy by employing machine learning algorithms trained on a diverse dataset of medical images to recognize patterns indicative of abnormalities such as tumors, fractures, and tissue irregularities. The system reduces false positives and negatives by using multi-level verification and adaptive learning mechanisms. It supports integration with hospital systems and offers an intuitive user interface for effective result interpretation. This invention addresses key challenges in medical diagnostics by offering a reliable, efficient, and scalable solution for automated detection of abnormalities in radiological images.
BRIEF DESCRIPTION OF DRAWINGS
[012] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in, and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure, and together with the description, serve to explain the principles of the present disclosure.
[013] In the figures, similar components, and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[014] Fig. 1 illustrates working flowchart associated with the proposed system, in accordance with the embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[015] 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.
[016] 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.
[017] 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.
[018] 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.
[019] 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.
[020] 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.
[021] In an embodiment of the invention and referring to Figure 1, the present invention relates to a novel and integrated system designed to detect abnormalities in X-ray and CT scan images. This system utilizes an advanced combination of innovative hardware components, artificial intelligence (AI), and Internet of Things (IoT) technologies to provide a robust, scalable, and accurate solution for medical diagnostics. The invention improves the diagnostic process by enhancing the precision of abnormality detection and reducing false positive and negative results.
[022] The image acquisition process is a foundational aspect of the system. High-resolution X-ray and CT scan images are captured using state-of-the-art imaging sensors integrated into advanced scanner units. These units incorporate high-sensitivity flat-panel detectors (FPDs) and custom-designed optical elements to capture detailed images with minimal noise and distortion.
[023] The image acquisition system is equipped with automated calibration hardware that adjusts imaging parameters based on patient characteristics. This ensures consistent image quality, reduces manual intervention, and enhances workflow efficiency. Data from the imaging hardware is transferred to the image pre-processing unit via high-speed fiber-optic communication links to maintain signal integrity.
[024] The image pre-processing unit consists of specialized graphics processing units (GPUs) and field-programmable gate arrays (FPGAs). These hardware accelerators perform critical image enhancement operations, such as contrast adjustment, noise filtering, and edge enhancement. The pre-processing ensures that images are optimized for accurate analysis by downstream components.
[025] Enhanced images are then transferred to the abnormality detection unit, which houses an array of hardware-based AI accelerators, including tensor processing units (TPUs) and neural processing units (NPUs). These accelerators execute sophisticated machine learning models to detect anomalies indicative of potential medical conditions.
[026] The abnormality detection algorithms deployed in this unit are based on deep learning architectures, such as convolutional neural networks (CNNs). The models are trained on an extensive dataset of labeled medical images, enabling the system to identify features such as tumors, lesions, fractures, and tissue irregularities with high sensitivity and specificity.
[027] The integration of adaptive machine learning models ensures that the system continuously improves its detection capabilities. Self-learning algorithms adjust to variations in imaging conditions and patient anatomy by leveraging real-world data through federated learning methods.
[028] The AI processing unit coordinates the decision-making process by aggregating results from multiple detection models. This unit employs an ensemble learning approach to combine outputs from different algorithms, enhancing the overall diagnostic accuracy. The system incorporates a verification step that validates detected abnormalities through multi-level evaluation.
[029] The IoT integration layer enables seamless connectivity between the diagnostic system and external medical systems, such as hospital information systems (HIS) and electronic medical records (EMR). This connectivity supports the automatic transfer of diagnostic results and patient information.
[030] IoT sensors embedded in imaging hardware monitor system health and performance in real time. Predictive maintenance algorithms analyze sensor data to detect potential hardware issues before they impact system functionality, ensuring uninterrupted diagnostic operations.
[031] Data security and privacy are critical aspects of the invention. The system incorporates end-to-end encryption for all data transmissions and employs biometric authentication for user access control. Role-based permissions limit access to sensitive data, ensuring compliance with healthcare regulations.
[032] The user interface and visualization module present detected abnormalities to healthcare professionals through high-resolution displays. The interface provides interactive tools for image manipulation, enabling manual verification and customization of analysis parameters.
[033] An advanced haptic feedback system integrated into the user interface offers tactile cues to guide radiologists in reviewing complex cases. This feature enhances diagnostic efficiency by providing additional sensory information.
[034] The data storage and management system utilizes a distributed architecture to store medical images and diagnostic data. Redundant storage nodes ensure data availability and integrity, while advanced compression algorithms reduce storage requirements without compromising image quality.
[035] A dedicated hardware-accelerated database management system handles high-throughput data transactions, ensuring efficient retrieval and storage of diagnostic information. This system supports real-time analytics and reporting functionalities.
[036] The invention’s scalability is demonstrated by its modular design, which allows components to be upgraded or expanded as needed. The system supports integration with additional diagnostic modalities, such as MRI and ultrasound, to create a comprehensive diagnostic platform.
[037] Performance validation of the system has been conducted through rigorous testing. Comparative studies show that the proposed system outperforms traditional diagnostic tools in terms of accuracy, speed, and reliability.
[038] Table 1 illustrates the improvements in diagnostic accuracy and processing speed achieved by the proposed system compared to conventional solutions:

[039] The interconnection between hardware components ensures efficient data flow and system performance. High-speed data buses link the image acquisition, pre-processing, and detection units, enabling real-time image analysis.
[040] Power-efficient design principles have been employed throughout the system, incorporating advanced cooling mechanisms and energy-saving modes to minimize operational costs.
[041] The system’s hardware components are designed for easy maintenance and serviceability. Modular components can be replaced independently, reducing system downtime during repairs.
[042] Extensive clinical trials have demonstrated the system’s efficacy in detecting a wide range of medical conditions. The results of these trials validate the system’s potential to significantly improve patient outcomes.
[043] The combination of advanced hardware and AI-driven software enables the system to operate effectively in diverse clinical environments, from large hospitals to remote healthcare facilities.
[044] The present invention represents a significant advancement in medical diagnostics, providing healthcare professionals with a powerful tool for accurate and efficient detection of abnormalities in radiological images.
[045] By integrating novel hardware components, AI algorithms, and IoT technologies, this system addresses critical challenges in medical imaging diagnostics, setting a new standard for accuracy, reliability, and operational efficiency.
, Claims:1. A system for detecting abnormalities in X-ray and computed tomography (CT) scan images, comprising:
a) an image acquisition module configured to capture high-resolution medical images using advanced imaging sensors and automated patient positioning systems;
b) an image pre-processing unit comprising hardware accelerators for noise reduction, contrast adjustment, and edge enhancement;
c) an abnormality detection unit utilizing tensor processing units (TPUs) and neural processing units (NPUs) to execute machine learning models for anomaly detection;
d) an AI processing unit for classifying detected abnormalities and aggregating outputs from multiple detection models;
e) an IoT integration layer connecting the system to hospital information systems (HIS) and electronic medical records (EMR);
f) a user interface module for real-time visualization of detected abnormalities;
g) a data storage and management system with distributed architecture for secure storage and retrieval of diagnostic data.
2. The system as claimed in claim 1, wherein the image acquisition module includes flat-panel detectors (FPDs) and high-sensitivity photodiodes to capture detailed images with minimal noise.
3. The system as claimed in claim 1, wherein the image pre-processing unit utilizes graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) for accelerated image enhancement.
4. The system as claimed in claim 1, wherein the abnormality detection unit applies convolutional neural networks (CNNs) trained on a diverse dataset of labeled medical images for identifying tumors, lesions, fractures, and tissue irregularities.
5. The system as claimed in claim 1, wherein the AI processing unit employs an ensemble learning approach to combine outputs from multiple algorithms to enhance diagnostic accuracy.
6. The system as claimed in claim 1, wherein the IoT integration layer enables remote monitoring and predictive maintenance through real-time sensor data analysis.
7. The system as claimed in claim 1, wherein the user interface module provides interactive tools for manual verification and customization of image analysis parameters.
8. The system as claimed in claim 1, wherein the data storage and management system employs compression algorithms to optimize storage efficiency while maintaining image quality.
9. The system as claimed in claim 1, wherein the system incorporates security features such as end-to-end data encryption and biometric authentication for access control.
10. The system as claimed in claim 1, wherein predictive maintenance algorithms analyze sensor data to detect hardware issues and alert maintenance personnel prior to system failures.