Brain Tumor Detection Using Hybrid Deep Learning Models

The rapid growth of artificial intelligence and deep learning technologies has greatly improved medical image analysis techniques. Brain tumors are considered one of the most critical types of neurological diseases and their early detection is considered crucial for their treatment. The proposed method aims to develop a deep learning-based system for brain tumor detection using different convolutional neural network models such as VGG16, ResNet18, InceptionV3, and DenseNet121. It works by finetuning different models using images of brain tumors using MRI scans and classifying them into different types such as glioma, meningioma, pituitary tumor, and no tumor. It is implemented using different deep learning models such as PyTorch and OpenCV, enabling users to enter their MRI scan images and obtain the prediction results. The experimental results have shown that the proposed model can accurately detect brain tumors, enabling doctors to make faster and accurate diagnosis.