A Triple-Level Ensemble-based Brain Tumor Classification Using Dense-ResNet in Association with Three Attention Mechanisms

Brain tumors, characterized by abnormal cell growth in the brain or central nervous system, pose a serious health concern. Accurate tumor type diagnosis is paramount for effective treatment. Conventionally, medical experts rely on manual evaluation of MR images, a process susceptible to human error and misdiagnosis. To address this challenge and enhance accessibility, our study explores a novel solution utilizing Dense-ResNet (DRN), a deep convolutional neural network (CNN) architecture. DRN introduces a Triple-Level Ensemble (TLE) approach, featuring three attention mechanisms. Specifically, our model harnesses features from three variations of two pre-trained CNN architectures: DenseNet (DN-121, DN-169, DN-201) and ResNet (RN-50, RN-101, RN-152), coupled with channel attention, soft attention, and squeeze-excitation attention. We rigorously evaluated our method by classifying 3064 T1-weighted MRI images across three brain tumor classes: Glioma, Meningioma, and Pituitary tumor. Impressively, our TLE approach achieved outstanding results, boasting an accuracy rate of 98.05% and more specifically, 98.14% for Glioma, 99.07% for Meningioma, and 97.14% for Pituitary tumor diagnosis. This automated approach not only reduces the risk of human error but also offers a robust and accurate means of brain tumor classification, holding great promise in the field of medical diagnostics.