Description:FIELD OF THE INVENTION
The present invention relates to an artificial intelligence (AI)-based system for the automated detection and classification of lung nodules in computed tomography (CT) scans. More specifically, the invention employs deep learning techniques, including convolutional neural networks (CNNs) and region proposal networks (RPNs), to enhance accuracy, reduce diagnostic time, and assist radiologists in detecting early-stage lung cancer.
BACKGROUND OF THE INVENTION
Lung cancer is one of the leading causes of cancer- related deaths worldwide, and early discovery significantly improves survival rates. still, detecting lung nodes, especially in their early stages, remains a grueling task for radiologists due to the complex and varied nature of nodes. The current styles of lung bump discovery primarily calculate on homemade examination of casket CT reviews, which is time- consuming, prone to mortal error, and hamstrung due to the sheer volume of reviews reused daily. likewise, the discovery of small or early- stage nodes, which are critical for early cancer opinion, is delicate to achieve with high delicacy using traditional individual tools. There's a clear need for an automated, dependable, and effective system that can help radiologists in the discovery and bracket of lung nodes in CT reviews. Being AI- driven results haven't yet reached the required position of perfection or scalability for wide clinical relinquishment.
Feature Proposed AI Solution Traditional/Previous Solutions
Detection Accuracy High, with deep learning models like CNNs and 3D CNNs Moderate, relying on radiologist interpretation or rule-based algorithms
Sensitivity and Specificity High, due to large annotated datasets and model training Lower, with frequent false positives and missed detections
Workflow Integration Integrated with radiology workflows to prioritize cases Manual or standalone, requiring radiologists to process every case
Scalability Scalable with cloud-based deployment options Limited to on-premise installations, not feasible for smaller facilities
Adaptability Adaptable, with continuous learning through radiologist feedback Static, with minimal or no adaptability after deployment
Processing Speed Real-time or near-real-time analysis using GPU acceleration. Slower, depending on manual review or older software
Data Utilization Utilizes large, standardized datasets for training and validation Limited data use, often missing larger datasets for model training
3D Image Processing 3D CNNs analyze full CT volumes, capturing inter-slice context Primarily 2D processing, missing spatial relationships between slices
False Positive Rate Reduced through advanced model training and multi-stage processing High, with CAD systems or manual methods often flagging benign nodules
Feedback Mechanism Includes a feedback loop for model improvement No feedback integration, static performance over time

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.
The present invention provides an AI-driven lung nodule detection and classification system that addresses the shortcomings of conventional diagnostic methods. The system employs deep learning algorithms to automatically analyze CT scans, identify potential lung nodules, and classify them as benign or malignant with high accuracy. By integrating CNNs, RPNs, and 3D convolutional networks, the invention enhances the accuracy and efficiency of nodule detection while minimizing false positives and false negatives.
The invention operates through a multi-step process involving data collection, preprocessing, model training, nodule detection, classification, and integration into radiology workflows. Large annotated datasets such as the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) are used to train the AI model, ensuring robust and generalizable performance. Data augmentation techniques further enhance the model's ability to recognize nodules of various shapes, sizes, and appearances.
The AI model processes CT scans using CNNs for feature extraction, RPNs for region proposal, and 3D CNNs for volumetric analysis, thereby capturing spatial relationships between slices. The system continuously learns from radiologist feedback, improving its diagnostic performance over time. Automated alerts notify radiologists of potential nodules, enabling early intervention and reducing diagnostic workloads. Additionally, the invention adheres to data privacy regulations such as HIPAA and GDPR, ensuring secure patient data processing.
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.
The proposed result solves the problem of lung bump discovery in CT reviews by using Artificial Intelligence (AI) and Deep literacy (DL) technologies to produce an automated, scalable, and accurate discovery 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: VARIABILITY IN BUMP SHAPES, SIZES, AND EXPOSURES
FIGURE 2: CONVOLUTIONAL NEURAL NETWORKS (CNNS) ARCHITECTURE
FIGURE 3: SYSTEM ARCHITECTURE
FIGURE 4: CT SCAN PRE-PROCESSING PIPELINE
FIGURE 5: REPRESENTATION OF LIDC-IDRI LUNG CT SCAN DATASET
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.
The proposed invention consists of an AI-based system that automates the detection and classification of lung nodules in CT scans. The system operates through a combination of machine learning algorithms and radiology integration to streamline the diagnostic process.
The invention begins with data collection, where CT scans from publicly available and proprietary datasets are curated. These images undergo preprocessing to remove noise, normalize pixel intensities, and segment the lung region. Data augmentation techniques such as rotation, scaling, and flipping are applied to enhance model robustness. The AI model is trained on these datasets using deep learning architectures, primarily CNNs, which extract hierarchical features relevant to lung nodule detection.
To localize nodules within the CT scans, the system employs RPNs, which propose candidate regions likely to contain nodules. These proposals are refined using additional CNN layers that analyze the texture, shape, and density of the detected regions. For volumetric data analysis, 3D CNNs process the entire CT scan stack, capturing inter-slice relationships and improving nodule localization accuracy.
Once nodules are detected, the system classifies them as benign or malignant based on learned features such as growth patterns and irregularities. The classification model is trained using supervised learning with annotated datasets, ensuring high sensitivity and specificity. The AI model's performance is evaluated using cross-validation techniques, and metrics such as recall, precision, and F1-score are used to optimize detection accuracy.
The invention is designed to be seamlessly integrated into radiology workflows. Upon detecting a potential nodule, the system generates an alert and highlights the region of interest for radiologist review. The radiologist can validate, modify, or reject the AI-generated findings, with feedback loops continuously refining the model’s accuracy. This hybrid approach ensures that AI serves as an assistive tool rather than a standalone diagnostic system.
To comply with regulatory requirements, the system implements robust data encryption and access control mechanisms, ensuring patient confidentiality. Clinical validation trials may be conducted to achieve regulatory approvals from authorities such as the FDA before deployment in hospitals and diagnostic centers.
The proposed result solves the problem of lung bump discovery in CT reviews by using Artificial Intelligence (AI) and Deep literacy (DL) technologies to produce an automated, scalable, and accurate discovery system. Then is how the idea works and how it's enforced
1. Data Collection and Preprocessing
Dataset Selection The system is trained on large, annotated datasets like the LIDC- IDRI( Lung Image Database Consortium and Image Database Resource Initiative), which provides thousands of annotated CT checkup images containing lung nodes.
Data Augmentation Given the variability in bump shapes, sizes, and exposures, the dataset undergoes data addition ways similar as gyration, flipping, and scaling. This increases the model's capability to generalize across different lung bump appearances, perfecting delicacy across different cases.
Preprocessing The CT images are preprocessed to concentrate on the lung regions and insure that the pixel intensity is regularized, barring noise and inapplicable information. This reduces the complexity of the input data and enhances the model's capability to learn applicable features.
2. Model Architecture
Convolutional Neural Networks (CNNs) The model armature primarily uses CNNs, which are designed to reuse and dissect image data. CNNs exceed at detecting complex patterns and shapes in images, making them ideal for detecting lung nodes.
Concentrated Approach The CNN layers precipitously learn spatial scales and features from the raw input images, from low- position edges to advanced- position complex structures like nodes.
Region Offer Networks (RPNs) The AI system integrates Region Offer Networks (RPNs), a crucial element in object discovery models (similar as Faster R- CNN), which helps the model propose regions within the CT images that are most likely to contain lung nodes. These proposed regions are also further anatomized for accurate bracket.
Localization RPNs help detect where the nodes are within the 2D slices of the CT checkup, which is pivotal for determining their size, shape, and implicit malice.
The below flowchart deals with the working pattern (flow chart) of the CNNs Architecture.
3D Convolutional Neural Networks (3D CNNs): Since CT reviews are volumetric (3D) data, the system incorporates 3D CNNs that can reuse and learn from the successional slices of a CT checkup. This helps the model prisoner spatial connections between slices and provides a more accurate understanding of the bump’s environment in 3D space.
Volumetric Data Processing This enables the model to descry nodes that may not be egregious in a single 2D slice but can be linked when considering the 3D relationship between slices.
3. Discovery and Bracket
Bump Discovery: The system uses deep literacy algorithms to descry lung nodes across the CT checkup slices. The AI model flags implicit nodes by pressing regions with a high liability of being nodes.
Bracket Once nodes are detected, the system classifies them into different orders, similar as benign or nasty, grounded on the appearance, size, and growth patterns observed in the 3D environment. The system uses double bracket (nasty vs. benign) or multi-class bracket (farther categorizing into subtypes) to assess the threat associated with each bump.
Model Tuning: The AI model is trained on labeled data where nodes are manually annotated by experts. Over time, it learns to identify features that separate benign from nasty nodes.
4. Model Evaluation
Cross-Validation: The system is strictly validated using ways like-fold cross-validation to ensure that it performs well across a variety of datasets. This step helps minimize overfitting and maximizes the robustness of the model.
Performance Metrics: The model is estimated using perceptivity (recall), particularity, delicacy, and F1- score. perceptivity is particularly important to reduce false negatives (missing cancerous nodes), while particularity ensures that benign nodes aren't flagged unnecessarily.
5. Integration into the Radiology Workflow
Automated Flagging: The AI model is integrated into the radiology department's being systems, where it automatically flags reviews with suspected lung nodes. This is done through an alert system that notifies radiologists when a checkup contains a high probability of a bump, helping prioritize cases that need immediate attention.
Radiologist Verification: The AI serves as a pre-screening tool. Radiologists review the flagged cases, furnishing the final opinion grounded on their moxie. This reduces the workload for radiologists by fastening their attention on potentially critical cases while allowing them to handle complex cases more efficiently.
Nonstop literacy: The system supports feedback circles, where new annotated reviews and radiologist feedback are used to modernize and upgrade the AI model periodically. This ensures that the model improves over time, conforming to new cases and evolving medical knowledge.
6. Regulatory Compliance and Safety
Data sequestration and Security: The system ensures that all patient data is reused and stored in compliance with data sequestration regulations similar as HIPAA in the U.S. and GDPR in Europe.
Clinical confirmation: The system undergoes thorough clinical confirmation and may bear nonsupervisory blessing from health authorities (similar as the FDA in the U.S.) before it can be extensively enforced in clinical practice.
How It Works:
1. CT Scan Upload: A chest CT scan is uploaded into the system.
2. Preprocessing: The scan is pre-processed to extract lung regions and normalize pixel values.
3. Nodule Detection: The AI model processes the scan, detecting potential nodules using CNNs, RPNs, and 3D CNNs.
4. Alert and Classification: Detected nodules are classified (benign or malignant), and a report is generated with an alert for the radiologist.
5. Radiologist Review: The radiologist reviews the flagged scans and confirms or adjusts the findings, potentially using the feedback to retrain and refine the AI model.
NOVELTY
1. Integration of Multiple Deep Learning Architectures.
2. Continuous Learning with Feedback Loops.
3. Seamless Integration into Radiology Workflows.
4. Cloud-Based Scalability.
5. Enhanced Accuracy with Contextual 3D Analysis.
6. Focus on Interpretability and Radiologist Trust.
 
ADVANTAGES OF THE INVENTION
1. Increased Detection Accuracy:
o The AI-driven solution leverages advanced deep learning models, such as Convolutional Neural Networks (CNNs) and 3D CNNs, which provide superior accuracy in detecting small and subtle lung nodules compared to traditional image analysis methods.
o Traditional methods often rely on manual review or rule-based approaches, which are more prone to missing nodules due to variability in nodule appearance.
2. Enhanced Sensitivity and Specificity:
o Unlike previous approaches, the proposed AI solution is trained on large, annotated datasets like LIDC-IDRI, allowing it to achieve high sensitivity (recall) and specificity in identifying true nodules while minimizing false positives.
o Traditional CAD (Computer-Aided Detection) systems can detect nodules but often result in a high number of false positives, causing unnecessary follow-up tests and anxiety for patients.
3. Workflow Efficiency:
o The proposed solution automatically prioritizes suspicious cases, allowing radiologists to review urgent cases first, which helps improve overall diagnostic efficiency and patient outcomes.
o Previous solutions, especially manual reviews, are time-intensive and can delay diagnosis, particularly in high-volume clinical settings.
4. Scalability with Cloud and Feedback Integration:
o The AI model can be deployed on a cloud-based system, making it scalable and accessible to smaller healthcare facilities with limited resources.
o Continuous feedback from radiologists can be used to fine-tune and improve the AI model, unlike conventional methods, which remain static and do not improve with usage.
5. Automated 3D Analysis of CT Scans:
o Utilizing 3D CNNs allows the model to analyse CT scans in a volumetric format, capturing spatial relationships across slices for improved accuracy. Prior solutions were typically limited to 2D image analysis, which missed contextual data between slices.
6. Real-Time Data Processing:
o With advancements in GPU processing, the AI model can analyse CT scans in real-time or near-real-time, providing quicker results than traditional methods, which require manual time to examine each scan.
, Claims:WE CLAIM:
1. An AI-based system for detecting and classifying lung nodules in CT scans, comprising:
o A data preprocessing module configured to segment lung regions and normalize CT scan images.
o A convolutional neural network (CNN) model trained to extract hierarchical features from CT scan images.
o A region proposal network (RPN) configured to identify candidate regions for nodule detection.
o A 3D convolutional neural network (3D CNN) model for volumetric data analysis to improve nodule localization.
o A classification module trained to differentiate between benign and malignant nodules based on learned image features.
2. The system as claimed in claim 1, wherein the data preprocessing module employs noise reduction, pixel intensity normalization, and lung region segmentation.
3. The system as claimed in claim 1, wherein the CNN model is trained using a dataset including annotated CT scans from medical databases.
4. The system as claimed in claim 1, wherein the RPN is configured to propose candidate regions likely to contain nodules based on texture, shape, and density.
5. The system as claimed in claim 1, wherein the 3D CNN processes volumetric CT scan data to capture inter-slice relationships and enhance detection accuracy.
6. The system as claimed in claim 1, wherein the classification module uses deep learning techniques to assess the malignancy of detected nodules.
7. The system as claimed in claim 1, further comprising an alert module that notifies radiologists of detected nodules with high malignancy probability.
8. The system as claimed in claim 1, further comprising a feedback mechanism that integrates radiologist input to refine model accuracy over time.
9. The system as claimed in claim 1, wherein the AI model is continuously updated using new annotated datasets to improve detection accuracy.