High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model

Aim: We originated a high-performance multiple sclerosis classification model in this study. Method: The dataset was segmented into training, validation, and test sets. We used AlexNet as the basis model, and employed transferred learning to adapt AlexNet to classify multiple sclerosis brain image in our task. We tested different settings of transfer learning, i.e., how many layers were transferred and how many layers were replaced. The learning rate of replaced layers are 10 times of that of transferred layer. We compare the results using five measures: sensitivity, specificity, precision, accuracy and F1 score. Results: We found replacing the FC_8 block in original AlexNet can procure the best performance: a sensitivity of 98.12%, a specificity of 98.22%, an accuracy of 98.17%, a precision of 98.21%, and an F1 score of 98.15%. Conclusions: Our performance is better than seven state-of-the-art multiple sclerosis classification approaches.