Brain Tumor Detection by Fusion Techniques

Brain tumors are a serious medical condition, and their early detection is crucial for successful treatment and patient survival. Brain tumors can be detected and located using medical imaging techniques including MRI, CT, and PET. Brain tumor identification has been automated using machine learning techniques. However, they face several challenges, including image variability, small dataset size, inter-observer variability, and computational complexity. Researchers have created a variety of methods to solve these problems, including transfer learning, data augmentation, and ensemble learning. The study established a brand-new paradigm for classifying brain tumors from MRI scans. The proposed framework consisted of several stages, including noise removal, segmentation, data augmentation, feature extraction, and a network ThinNet15 classification network. Accuracy, precision, recall, and F-score were just a few of the performance criteria used to assess the framework and compare it to comparable models already in use. The outcomes demonstrated that the suggested framework outperformed the other models, achieving a remarkable accuracy percentage of 98.8372%. The results suggest that ThinNet15 is a promising architecture for image classification applications with limited resources. This research holds great potential for improving the diagnosis and treatment of brain tumors, with the use of non-invasive MRI imaging techniques.