A Hybrid AI Model for Early Detection of Diseases from Medical Imaging Data

Abstract

The proposed project aims to develop a Smart AI System for the early detection of diseases using medical imaging data such as X-rays, CT scans, and MRIs. Leveraging the power of deep learning and convolutional neural networks (CNNs), the system is trained to identify patterns and anomalies indicative of diseases like cancer, pneumonia, and neurological disorders. The model is integrated into a user-friendly interface that enables real-time image analysis, improving diagnostic accuracy and reducing the workload on medical professionals. The system also provides automated reporting and alerts for high-risk cases, supporting timely and effective medical intervention. This intelligent diagnostic assistant is designed to enhance healthcare delivery, especially in remote and underserved areas. This intelligent diagnostic tool aims to reduce diagnostic errors, shorten evaluation time, and assist radiologists and physicians by serving as a second opinion. Moreover, the system can be deployed in telemedicine applications, making it especially beneficial in rural or under-resourced healthcare settings where access to medical experts is limited. Overall, the proposed solution offers a cost-effective, scalable, and reliable approach to improving diagnostic workflows in modern healthcare systems.

Introduction

The paper presents a Smart AI System designed to assist in the early detection of diseases through medical imaging using Artificial Intelligence and deep learning. Manual interpretation of medical images such as X-rays, CT scans, and MRIs is time-consuming, prone to error, and limited by the shortage of skilled radiologists, particularly in under-resourced regions. To address these challenges, the proposed system leverages Convolutional Neural Networks (CNNs) to automatically detect abnormalities with high accuracy and speed, supporting clinicians with reliable decision-making tools.

The literature review highlights significant advances in AI-based medical imaging, including successful applications in lung disease, brain tumor classification, and breast cancer detection, often achieving performance comparable to or exceeding that of expert radiologists. Despite these advances, challenges such as data privacy, interpretability, and clinical validation remain.

The proposed methodology follows a structured pipeline involving data collection from public and clinical datasets, expert annotation, preprocessing (normalization, augmentation, segmentation), CNN-based model development using transfer learning, and rigorous training and evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The system is deployed via a user-friendly web or desktop interface that provides real-time predictions, visualized regions of interest (ROI), and automated diagnostic reports. A post-deployment feedback loop enables continuous learning and improvement.

Experimental results demonstrate strong performance across multiple diseases, with high accuracy (≥93%), AUC-ROC values above 0.95, and real-time inference under one second per image. ROI visualization improves interpretability and reduces diagnostic time for clinicians. Compared to manual review, the system shows a 3–5% improvement in accuracy while offering greater consistency, scalability, and accessibility.

Overall, the Smart AI System provides a scalable, cost-effective, and reliable solution for early disease detection, enhancing diagnostic speed, accuracy, and healthcare accessibility. While limitations remain—such as data diversity, handling of 3D imaging, and regulatory requirements—the system shows strong potential for integration into clinical workflows and future expansion through multi-modal data fusion and continuous learning.

Conclusion

The development of the Smart AI System for Early Detection of Diseases Using Medical Imaging demonstrates a transformative approach to modern diagnostic workflows. By harnessing deep learning— particularly convolutional neural networks—and integrating them into an end?to?end platform, the system achieves rapid, automated analysis of X?rays, CT scans, and MRIs with performance metrics (AUC?ROC > 0.95, overall accuracy ~93.8%) that rival or exceed those of expert radiologists. Its real?time inference capability and automated ROI highlighting substantially reduce interpretation time and support clinicians with standardized, confidence?scored reports. Beyond improving diagnostic accuracy and speed, the platform’s scalable, cloud?based architecture and telemedicine interface extend access to quality radiology services in underserved regions. While challenges remain —such as expanding data diversity, incorporating 3D volumetric analysis, and securing regulatory approvals— the proposed system lays a solid foundation for continuous learning and future enhancements. Ultimately, this Smart AI System holds the potential to democratize early disease detection, reduce healthcare disparities, and empower medical professionals with reliable decision?support tools.

References

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Copyright

Copyright © 2026 P T Prithivirajan, A Stephalin Aksha, S Subaash. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.