MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641070321 A) filed by Jpl Research And Innovation Consultancy; Dr. Harikrushna Gantayat; Villuri Anusha; Narasimhula Balayesu; Vikash Sawan; Renuka Kondabala; Krishna Kishore Thota; Dr. Sanjay Kumar Suman; and Dr. L. Bhagyalakshmi on June 04, 2026, for Automated Medical Imaging Analysis And Disease Detection Using Deep Learning Models.
Inventors include Dr. Harikrushna Gantayat; Villuri Anusha; Narasimhula Balayesu; Vikash Sawan; Renuka Kondabala; Krishna Kishore Thota; Dr. Sanjay Kumar Suman; and Dr. L. Bhagyalakshmi.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: The present invention relates to an automated medical imaging analysis and disease detection system that utilizes advanced deep learning models to improve the accuracy, efficiency, and reliability of medical diagnosis. The system is designed to process and analyze medical images obtained from various imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), mammography, retinal imaging, and digital pathology scans. By employing sophisticated artificial intelligence techniques such as convolutional neural networks, transformer-based architectures, attention mechanisms, and automated feature extraction algorithms, the invention is capable of identifying, classifying, localizing, and segmenting disease-related abnormalities with high precision. The invention incorporates image preprocessing, feature learning, disease classification, lesion detection, severity assessment, and explainable artificial intelligence modules to provide comprehensive diagnostic support. The system can detect a wide range of medical conditions, including cancers, cardiovascular disorders, neurological diseases, respiratory infections, retinal abnormalities, and other pathological conditions. Additionally, it generates interpretable outputs such as heatmaps, confidence scores, and highlighted regions of interest to enhance transparency and clinician trust. The invention further supports integration with electronic health records and clinical databases for context-aware analysis. By reducing diagnostic workload, minimizing human error, enabling early disease detection, and supporting clinical decision-making, the invention offers a scalable and intelligent solution for modern healthcare diagnostics.
Disclaimer: Curated by HT Syndication.
