EfficientNetV2 based for MRI brain tumor image classification

An accurate and timely diagnosis is of utmost importance when it comes to treating brain tumors effectively. To facilitate this process, we have developed a brain tumor classification approach that employs transfer learning using a pre-trained version of the EfficientNet V2 model. Our dataset comprises brain tumor images that have been categorized into four distinct labels: tumor (glioma, meningioma, pituitary) and normal. As our base model, we employed the EfficientNet V2 model with variations of B0, B1, B2, and B3 for experiments. To adapt the model to our number of label categories, we modified the final layer and retrained it on our dataset. Our optimization process involved using Adam's algorithm and the categorical cross-entropy loss function. We conducted experiments in multiple stages, which involved randomizing the dataset, pre-processing, training the model, and evaluating the results. During the evaluation, we used appropriate metrics to assess the accuracy and loss of the test data. Furthermore, we analyzed the performance of the model by visualizing the loss and accuracy curves throughout the training process. Our extensive experimentation involving dataset randomization, pre-processing, model training, and evaluation has yielded remarkable results. Through relevant evaluation metrics and visualization of loss and accuracy curves, we have achieved impressive accuracy and loss rates on test data. Our research has led us to the successful classification of brain tumors using the EfficientNet V2 models with B0, B1, B2, and B3 variations. Additionally, our use of a confusion matrix has allowed us to assess the classification ability of each tumor category. This breakthrough research has the potential to greatly enhance medical diagnosis by utilizing transfer learning techniques and pre-trained models. We hope that this approach can help detect and treat brain tumors in their early stages, ultimately leading to better patient outcomes.