Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital - Mulyana, Dadang Iskandar; Alifah, Rifdah (Jurnal Indonesia : Manajemen Informatika dan Komunikasi, 2024)
The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.