Integration of MobileNet and YOLO for Enhanced Brain Tumour Detection and Localization

Computer-Aided Detection (CAD) systems for brain MRI analysis utilize various AI techniques to assist radiologists in interpreting images and identifying abnormalities. These systems require training on extensive datasets with diverse brain pathologies to ensure accurate detection and classification of different diseases. In this study, the MobileNet architecture was used to classify brain tumours. Additionally, YOLOv4 and YOLOv5 architectures were implemented to label and visualize the tumours. Performance metrics, including Precision, Recall, F1 Score, and MAP, were evaluated. The coding for this work was developed in Python using the TensorFlow platform, and simulations were conducted on Google Colab.