MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641071908 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed To Be on June 10, 2026, for Image-Based Cervical Cancer Classification And Prediction Using Cnn Architecture.
Inventors include Dr. Y. Madhaveelatha; Ms. P Sindhuja; Ms. Srilavanya Sajja; Ms Boya Vijaya Durga; Ms. Sailaja Madhu; Dr. G. Mohan Ram; Dr. Tirumala Paruchuri; and Dr. Kundurti Sai Chand.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: The present invention discloses an intelligent medical image analysis system named PixelToPrognosis, designed for the automated classification and prediction of cervical cancer using deep learning techniques. Cervical cancer remains one of the leading causes of cancer-related deaths among women worldwide, particularly in regions where access to expert diagnostic facilities is limited. Conventional screening methods such as Pap smear tests and microscopic examinations rely heavily on manual analysis by medical professionals, which can be time-consuming and prone to diagnostic inconsistencies. The proposed invention addresses these limitations by integrating deep learning with automated medical image processing to support faster and more reliable diagnosis. The system operates through a structured framework that connects the medical image acquisition stage with the clinical decision-making stage. Instead of relying on manual observation or handcrafted features, the invention utilizes a Convolutional Neural Network (CNN) architecture capable of learning complex spatial patterns directly from cervical cell images. Initially, the images undergo preprocessing steps such as normalization, resizing, and noise reduction to ensure consistent input data. The CNN model then analyzes the processed images and extracts hierarchical features representing cellular structures. Based on these features, the system classifies the images into categories such as normal, pre- cancerous, or cancerous cells, thereby assisting healthcare professionals in identifying abnormal conditions. Furthermore, the system supports scalable deployment in healthcare environments such as hospitals, diagnostic laboratories, and telemedicine platforms. By enabling automated screening of cervical cell images, the invention reduces the dependency on manual interpretation and accelerates the diagnostic process. The system can process large volumes of medical images efficiently and generate predictive insights that assist clinicians in early detection and treatment planning. As a result, the invention contributes to improved healthcare accessibility, timely diagnosis of cervical cancer, and better patient outcomes
Disclaimer: Curated by HT Syndication.
