BioSegNet: Pioneering Convolutional Approaches in Medical Imaging

The integration of machine learning in biomedical imaging has significantly advanced the detection of tumors in brain using MRI scans. While traditional manual methods are effective, they often suffer from human error, inefficiency, and variability in diagnostic accuracy. BioSegNet addresses these challenges by introducing a CNN-based approach for automated detection of tumor in brain, segmentation, and classification of different types of tumors in MRI scans. This system reduces reliance on subjective interpretation and minimizes delays associ-ated with manual analysis. Built with TensorFlow Lite, BioSegNet ensures efficient deployment on edge devices and cloud platforms, making it versatile and accessible. Its user-friendly Streamlit-based web interface caters to users with varying technical expertise, enhancing its practical application in clinical settings. Extensive testing shows BioSegNet's ability to deliver accurate, real-time tumor analysis, offering a valuable tool for improving diagnostic accuracy and reducing errors. By optimizing for edge and cloud deployment, BioSegNet makes advanced diagnostic tools more accessible to medical professionals globally, contributing to better patient outcomes and timely treatments.