Developed a novel multi-label MRI image analysis system utilizing Convolutional Neural Networks (CNNs) for highly accurate brain tumor detection. Achieved 96.5% detection accuracy on a dataset of 2,500 MRI scans. The system employs a fine-tuned VGG16 architecture, significantly enhancing diagnostic precision and accelerating clinical decision-making. This innovation aims to reduce radiologist workload and improve early patient intervention.

Engineered an innovative real-time communication system for specially-abled users, integrating Deep Learning and CNN-based image processing. The system primarily leverages dynamic hand gesture recognition and facial expression analysis. It demonstrated 92% recognition accuracy in user testing, with participants reporting a 30% improvement in communication speed. Key accessibility features include customizable gesture libraries and integrated text-to-speech conversion, significantly enhancing interactive capabilities for individuals with speech impediments.