AlexNet-NDTL: Classification of MRI brain tumor images using modified AlexNet with deep transfer learning and Lipschitz-based data augmentation

Deep learning is frequently used to classify medical images. Surgeons may know the type of tumor before doing surgery on a patient. Transfer learning was used to alleviate the overfitting issue of deep networks in classification since the training samples, such as a brain MRI dataset, were insufficient. To overcome this issue, We introduce a new deep-learning methodology for the categorization of MRI brain tumor images. This method combines a unique data augmentation model with modified AlexNet and network-based deep transfer learning. We used Lipschitz-based data augmentation on a dataset, and the output of the augmentation model was fed into a modified AlexNet that uses network-based deep transfer learning to extract features from a dataset. The proposed model is trained and tested using the BraTS 2020 and Figshare datasets. The proposed model's performance is assessed using sensitivity, specificity, accuracy, precision, F1-score, and MCC. The proposed model yields superior results.