Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Abstract

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.