Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture

The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other organs spreads to the brain. This is because identification and staging of such tumours are critical because basically all aspects involving a patient's disease entail accurate diagnosis as well as the staging of the tumour. Image segmentation is incredibly valuable to medical imaging since it can make possible to simulate surgical operations, diseases diagnosis, anatomical and pathologic analysis. This study performs the prediction and classification of brain tumours present in MRI, a combined classification and localization framework model is proposed connecting Fully Convolutional Neural Network (FCNN) and You Only Look Once version 5 (YOLOv5). The FCNN model is designed to classify images into four categories: benign - glial, adenomas and pituitary related, and meningeal. It utilizes a derivative of Root Mean Square Propagation (RMSProp)optimization to boost the classification rate, based upon which the performance was evaluated with the standard measures that are precision, recall, F1 coefficient, specificity and accuracy. Subsequently, the YOLOv5 architectural design for more accurate detection of tumours is incorporated, with the subsequent use of FCNN for creation of the segmentation's masks of the tumours. Thus, the analysis proves that the suggested approach has more accuracy than the existing system with 98.80% average accuracy in the identification and categorization of brain tumour. This integration of detection and segmentation models presents one of the most effective techniques for enhancing the diagnostic performance of the system to add value within the medical imaging field. On the basis of these findings, it becomes possible to conclude that the advancements in the deep learning structures could apparently improve the tumour diagnosis while contributing to the finetuning of the clinical management.