Intellectual Property India Publishes Patent Application for A System And Method For Intelligent Medical Imaging Analysis And Disease Detection Using Deep Learning

MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641076523 A) filed by Meenakshi Academy Of Higher Education And Research on June 20, 2026, for A System And Method For Intelligent Medical Imaging Analysis And Disease Detection Using Deep Learning.

Inventors include Dr. Jaiganesh S; Dr. Nivya Mary A; Poongodi V; Koushik Kumar N; Kavitha M; and Nafrin Az.

The application for the patent was published on July 03, 2026, under issue no. 27/2026.

Abstract: ABSTRACT A System and Method for Intelligent Medical Imaging Analysis and Disease Detection Using Deep Learning The present disclosure is directed to a system and method for intelligent medical imaging analysis and disease detection through context- aware multimodal diagnostic fusion and dynamic confidence optimization. The system includes a medical image acquisition module, an adaptive imaging quality enhancement module, an anatomical context mapping module, a multimodal clinical fusion engine, a disease detection and localization engine, a diagnostic confidence optimization engine, a disease progression prediction module, an explainable diagnostic reasoning module, a dynamic imaging protocol recommendation module, and a federated medical knowledge learning module. The system may be adapted to receive medical images and associated clinical information, perform image enhancement, generate anatomical context representations, integrate multimodal diagnostic data, identify pathological conditions, and calculate diagnostic confidence metrics . In case of detected diagnostic uncertainty, the system automatically suggests further diagnostic procedures. The system also offers explainable diagnostic outputs and incrementally enhances disease intelligence models via privacy-preserving federated learning. The disclosed invention increases diagnostic accuracy and reliability, explainability, disease progression forecasting, clinical decision support, and decreases diagnostic uncertainty and improves patient care outcomes.

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