Abstract:
Deep learning (DL) technologies are currently a trendy topic because they aim to understand
concepts more precisely by analyzing data at a high level of abstraction through non-linear
understanding. This enables them to achieve high performance in image classification, especially
for tasks related to medical diagnoses, like analyzing images of the brain's histopathology.
Neural networks (CNN) are the most used deep learning models for diagnosing and analyzing
medical imaging data. CNN must be implemented at a significant computational expense, and
various parameters may need to be adjusted. In this study, we propose a hybrid strategy by fitting
the VGG16 pre-trained model with the LSVM classifier for classification of a brain image as
normal or abnormal. An accuracy of 98.24% is the demonstrated performance using this
proposed method on the test set. It was found to be better in terms of accuracy, error rate,
sensitivity, F-1 score, and specificity, according to the experimental results.