MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641070797 A) filed by Dr. Ghazala Sultan, Noida Institute Of Engineering And Technology; Nannuri Suresh, Vignan Institute Of Technology And Science; Dr. G. Bindu Madhavi, Geethanjali College Of Engineering And Technology; Lavanya Dalavai, Malineni Lakshmaiah Women'S Engineering College; and Vijaya Krishna Sonthi, Koneru Lakshmaiah Education Foundation on June 06, 2026, for An Explainable Hybrid Deep Learning-Based Predictive Framework For Early Breast Cancer Diagnosis And Intelligent Clinical Data Analysis.
Inventors include Dr. Ghazala Sultan, Noida Institute Of Engineering And Technology; Nannuri Suresh, Vignan Institute Of Technology And Science; Dr. G. Bindu Madhavi, Geethanjali College Of Engineering And; Lavanya Dalavai, Malineni Lakshmaiah Women'S Engineering College; and Vijaya Krishna Sonthi, Koneru Lakshmaiah Education Foundation.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: The present invention discloses an explainable hybrid deep learning-based predictive framework for early breast cancer diagnosis and intelligent clinical data analysis. The framework integrates multimodal healthcare data including medical images, pathological reports, laboratory findings, and patient clinical records. A preprocessing module performs data enhancement and normalization, while a feature extraction module employs deep convolutional and transformer-based architectures to derive discriminative representations. An optimization engine selects informative features for efficient analysis. A hybrid prediction model comprising CNN, BiLSTM, and attention mechanisms performs breast cancer classification and risk assessment. An explainability module incorporating SHAP, Grad-CAM, LIME, and attention visualization generates interpretable diagnostic insights for clinicians. The framework improves diagnostic accuracy, transparency, reliability, and decision support for early breast cancer detection. The invention provides an effective AI-driven healthcare solution suitable for clinical deployment and intelligent medical data analytics
Disclaimer: Curated by HT Syndication.
