Deep Learning Techniques for MRI Image-Based Performance Analysis of Brain Tumor Classification - Renuga S; Malathi P; Shamija Sherryl R.M.R; Anuradha T; Mishmala Sushith; Senthil Kumar A (Yayasan Riset dan Pengembangan Intelektual (YRPI), 2024)
Brain tumors can produce symptoms and indicators due to direct tissue death, localized invasion of the brain, or aftereffects from increased intracranial pressure. In order to identify images from the publicly available image dataset, this work combined multiple image feature sources using deep learning algorithms. The architecture of most classic convolutional neural networks (CNNs) consists of convolution modification and max-pooling of layers connected with several completely linked layers. The steps used in this system are pre-processing, segmentation, feature extraction, and classification. The preprocessing procedures of this investigation were used by the modified trimmed median filtering approach. U-Net segmentation is used to carry out the segmentation process. Features are then extracted using the wavelet transform method. In this study, MRI images of brain tumors, including meningnant and benign tumors, were detected and classified using the proposed CNN-based VGG16 model. The convolutional neural network (CNN) architectures employed in this investigation were guided by the VGG-16. The outcomes are assessed in terms of accuracy, precision, recall, and F1-score after the suggested model has been simulated. According on the test findings, the recommended approach may lead to 96.9% maximum recall, 97.4% maximum F1-score, 98.45% maximum accuracy, and 98.1% maximum precision.