Efficient ML Technique for Brain Tumor Segmentation, and Detection, based on MRI Scans Using Convolutional Neural Networks (CNNs)

Abstract

Experts need accurate segmentation and detection alongside the classification of Brain tumors from MRI images because this approach helps identify neurological problems early for timely treatment. Deep learning technology has made Convolutional Neural Networks (CNNs) effective in analyzing complex medical imaging challenges by developing automatic abilities to detect and categorize complex data features. This study used 1,251 Brain Tumor MRI images from BraTS2021 for model testing of CaPTk, 2DVNet, EnsembleNets, and ResNet50 towards brain tumor segmentation. The research utilized the DSC and HD metrics for its evaluation process. Importantly, EnsembleUNets achieved the minimum HD of 18 while reaching the maximum DSC of 0.92. The analysis of the radiomic feature confirmed that EnsembleUNets delivered the best CCC value at 0.75 together with the lowest RMSE at 0.52 and the highest TDI at 1.9 for tumor segmentation and classification in clinical practice. These findings show EnsembleUNets effectively perform brain tumor segmentation and classification and identification so healthcare professionals now have more effective guidance about implementing CNN-based clinical applications.

Keywords: Cyber-physical systems (CPSs). Security Protocols, Encryption, OpenVPN, IKEv2/IPsec, WireGuard, Quantum Computing