MUMBAI, India, Oct. 26 -- Intellectual Property India has published a patent application (202641123478 A) filed by J. J College Of Engineering And Technology on October 15, 2026, for Deep Learning-Based Automated Medical Image Analysis And Abnormality Detection System.
Inventors include Mrs A Apoorvavalli; Ms Rajeshwari S; Abinaya. N; Mrs Mahalakshmi P; Suganya. P; Yobu D; Mrs M Saranya; and Sathiya Priya T.
The application for the patent was published on October 23, 2026, under issue no. 43/2026.
Abstract: The present invention relates to a deep learning-based automated medical image analysis and abnormality detection system for identifying, localizing, and classifying potential abnormalities in medical images. The system comprises a medical image acquisition module, an image preprocessing module, a deep learning-based feature extraction module, an abnormality detection module, a classification module, and a visualization and reporting module. Medical images obtained from modalities including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound are processed to improve image quality, normalize intensity values, and reduce noise and artifacts. A trained deep learning model extracts hierarchical spatial and contextual features from the preprocessed images and detects regions potentially associated with abnormal tissue structures, lesions, tumors, fractures, or other image-visible abnormalities. The system employs confidence scoring and localization techniques to generate interpretable detection outputs and supports the generation of structured analysis reports for review by qualified healthcare professionals. A feedback mechanism may be incorporated to facilitate model refinement using verified annotations and validated image data. The proposed invention improves the consistency, efficiency, and reproducibility of medical image assessment by assisting healthcare professionals in preliminary abnormality screening and diagnostic decision support. The system is intended to function as an assistive tool and does not independently replace clinical diagnosis or professional medical judgment.
Disclaimer: Curated by HT Syndication.
