A hybrid machine learning Method for image classification

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.