YOLOv7 for brain tumour detection using morphological transfer learning model

An accurate diagnosis of a brain tumour in its early stages is required to improve the possibility of survival for cancer patients. Due to the structural complexity of the brain, it has become very difficult and tedious for neurologists and radiologists to diagnose brain tumours in the initial stages with the help of various common manual approaches to tumour diagnosis. To improve the performance of the diagnosis, some computer-aided diagnosis-based systems are developed with the concepts of artificial intelligence. In this proposed manuscript, we analyse various computer-aided design (CAD)-based approaches and design a modern approach with ideas of transfer learning over deep learning on magnetic resonance imaging (MRI). In this study, we apply a transfer learning approach with the object detection model YOLO (You Only Look Once) and analyse the MRI dataset with the various modified versions of YOLO. After the analysis, we propose an object detection model based on the modified YOLOv7 with a morphological filtering approach to reach an efficient and accurate diagnosis. To enhance the performance accuracy of this suggested model, we also analyse the various versions of YOLOv7 models and find that the proposed model having the YOLOv7-E6E object detection technique gives the optimum value of performance indicators as precision, recall, F1, and mAP@50 as 1, 0.92, 0.958333, and 0.974, respectively. The value of mAP@50 improves to 0.992 by introducing a morphological filtering approach before the object detection technique. During the complete analysis of the suggested model, we use the BraTS 2021 dataset. The BraTS 2021 dataset has brain MR images from the RSNA-MICCAI brain tumour radiogenetic competition, and the complete dataset is labelled using the online tool MakeSense AI.