A Comparative Analysis of Deep Learning Architectures for Efficient Brain Tumor Detection

This article studies the effectiveness of deep learning (DL) algorithms in detecting brain tumors, focusing on disorders such as “Glioma-Tumor,” “Meningioma-Tumor,” “PituitaryTumor,” and “No-Tumor.” Magnetic Resonance Imaging (MRI) is the primary tool for identifying brain tumors, and the paper proposes a convolutional neural network (CNN) architecture for efficient tumor detection. The study explores various CNN models, including DenseNet121, ResNet50V2, DenseNet201, EfficientNetB2, VGG16, and MobileNet, which enhance classification accuracy. The models demonstrate high precision, recall, F1-score, sensitivity, and specificity in predicting brain tumor conditions.