Optimizer-Aware Deep Learning for Brain Tumor Classification: A Study Using AlexNet to EfficientNetB0

One of the most lethal diseases in the world is brain tumors. Convolutional neural networks (CNNs) and other deep learning-based methods are vital for accurate diagnosis because of their remarkable capacity for learning and prediction. These techniques are widely used in image processing, computer vision, and medical diagnostic tasks such as classification, segmentation, and object detection. In this study, we use the publicly available Figshare brain tumor dataset to compare the performance of different pre-trained deep learning models, including AlexNet, VGG16, VGG19, ResNet50, Xception, InceptionV3, DenseNet121, MobileNetV1, and EfficientNetB0, on four optimizers, such as adaptive moment estimation (Adam), stochastic gradient descent (SGD), root mean square propagation (RMSProp), and Nesterov-accelerated adaptive moment estimation (Nadam). The experimental results show that the VGG19 and EfficientNetB0 models performed exceptionally well with the Adam optimizer, achieving an overall accuracy of 99.13% and a misclassification rate of 0.87%. Additionally, receiver operating characteristic (ROC) curves were calculated, with the EfficientNetB0 model achieving an area under the curve (AUC) value of 100% for each class. It also demonstrated excellent performance on the test images, with a testing accuracy of 99.61%.