Deep transfer learning model for brain tumor segmentation and classification using UNet and chopped VGGNet

Abstract

Brain tumors (BT) are a leading cause of cancer-related mortality worldwide, underscoring the critical need for early and precise detection to improve patient survival rates. Computer-aided diagnosis (CAD) plays a pivotal role in early BT detection by providing medical experts with valuable information image analysis. Various researchers have developed distinct methodologies, drawing from both machine and deep learning approaches. ML relies on manual feature analysis, which entails a time-intensive procedure of selecting an optimal feature extractor and necessitates domain experts with a deep understanding of feature selection. Conversely, deep learning methods exhibit superior performance compared to ML owing to their end-to-end, automated, high-level, and robust attribute mining capabilities. In this study introduced an innovative two-stage framework designed for the automatic classification of BT. In the initial stage, utilize U-Net models to conduct BT segmentation as part of the pre-processing step. Subsequently, in the second stage, utilize the improved BT images as input for a transfer learning-based model known as visual geometry group neural network (VGGNet), which excels in BT classification. The experimental analysis shows that the proposed approach has reported the average classification accuracy as 98.6%, 98.76%, and 99.45% for Meningioma, Glioma, and Pituitary BTs, respectively.