Convolutional Block Attention Module Integration into YOLO11 Architecture for MRI Image-based Brain Tumor Detection - Jendraja Husin Kotan; Yohannes; Hafiz Irsyad (Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika), 2026)
Brain tumor is one of the deadly diseases in the world that can affect anyone, this disease is characterized by the growth of abnormal cells or tissues in the brain, medically it can be life-threatening if not treated properly. Most tumor detection tasks are done by manual assessment by radiologists or pathologists where this work is time-consuming, so accurate and reliable detection is needed in the medical field in diagnosing brain tumors. The purpose of this study is to integrate CBAM on the YOLO11 architecture in detecting brain tumors and determine the performance of the brain tumor detection model using the YOLO11 architecture with CBAM integration. The method used to detect brain tumors is the YOLO11 architecture with CBAM integration. The dataset used is an image in the form of brain MRI. The results of this study indicate that the precision is 86.9%, recall is 86.2%, mAP50 is 91%, and mAP50-95 is 64% in the validation data and precision is 89.1%, recall is 92%, mAP50 is 79%, mAP50-95 is 51.6%, and F1 score is 90.5% in the test data which can be used to help medical personnel in detecting and treating brain tumors considering that this model has outstanding results, especially in the recall metric section which reaches 92% in the test data.