MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202541041243 A) filed by Rahul. A on April 29, 2025, for Brain Tumor Detection Using Vgg-16.
Inventors include Rahul. A; Prithiviraj D; Prithivraj S R; and Gokul Raj.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: ABSTRACT: Abstrnct-This project proposes an At-powered diagnostic system for the automated detection of brnin tumors from MRI images using the VGG-16 deep convolutional neural network. The primary objective of this research is to assist medical professionals by reducing the time required for diagnosis and increasing the accuracy of identifying tumorous regions. Manual identification of brain tumors can be prone to human error, especially when the tumour is at an early stage or the radiologist has limited experience. Hence, a machine learning-based approach can provide a second layer of confirmation and confidence.VGG-16, known for its strong performance in image classification, has been trained on a pre-labelled dataset of brain MRI images to differentiate between tumorous and non-tumorous cases. The system leverages convolutional layers to extract meaningful features from medical scans and classify them accordingly. To make the model's predictions interpretable, activation maps are generated, highlighting the exact location of suspected tumours. This transparency is crucial in medical diagnosis, allowing doctors to visually validate the Al's suggestions. The model requires minimal preprocessing and delivers results with high speed and accuracy, making it suitable for real-time clinical settings. It can be integrated into hospital workflows, mobile diagnostic tools, or telemedicine platforrns to support medical decisions in areas with limited access to specialists. Furthermore, this A I tool promotes early diagnosis, which is critical for improving patient survival rates and treatment outcomes Index Terms- Brain Tumor Detection, Deep Learning, VGG-I 6, Convolutional Neural Networks, MRI Imaging, Activation Maps, Medical Diagnosis, Image Classification, AI in Healthcare, Feature
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