ABSTRACT
Magnetic Resonance Imaging (MRI) early brain tumor categorization is crucial for the diagnosis of these conditions. Several varieties of diagnostic imaging modalities to detect brain tumours. MRI scans are a highly favoured option for these types of tasks due to their exceptional image quality. Deep learning, a subset of artificial intelligence, has revolutionized the field of automated medical image recognition. The goal of this project was to create a reliable and effective transfer learning technique-based system for MRI-based brain tumour classification. This article describes the development of a brain tumour diagnosis system using common deep learning architectures. Deep features from brain MRIs are extracted using pre-trained models as, Xception, and CNN. Two publicly available benchmark datasets from the internet were used in the experiment. To enhance training speed, accuracy and precision, the image dataset were cropped, pre-processed and enhanced images from the dataset. This study entails utilizing a brain MRI dataset to train and assess deep transfer learning models. This approach employs multiple performance metrics, including accuracy, sensitivity, precision, specificity, and F1-score, to assess its effectiveness. Based on the Xception architecture and utilizing the ADAM optimizer, our Convolutional Neural Network (CNN) model outperforms the other models in our testing data. On the MRI dataset, the Xception model achieved 99.66% accuracy, 99.68% sensitivity, 99.66% precision, 99.68% specificity, and 99.68% F1-score.
