Supervised Deep Learning Multi-Model ensemble of Computer Vision Transformer for Brain Tumor Detection
It is well known fact that in medical area, identifying the brain tumors properly through Magnetic Resonance Imaging (MRI) scans becomes essential for curing them within early time-frame and planning effectively for their treatments. While convolutional neural networks (CNNs) are popular in their work for finding local details in images, but, till now, they often have shortcomings on capturing broader context across the whole scan. New technology of Transformers, like the Swin model, can help eradicating these problems in understanding these global relationships but it also has some performance problem, when we use it standalone. To get rid of both issues, our study proposes SXM-Net, which is a hybrid deep ensemble model that utilizes a Swin Transformer model with CNN models especially Xception and MobileNetV2. Our approach setup classifies brain tumors into four types: glioma, meningioma, pituitary tumors, and normal (healthy brain with no tumor) tissue. Our proposed ensemble model expresses the superior experimental performance outcome in two different aspects: first one as compared to individual convolutional network model like Xception and mobileNetV2 seperately, and second one when we have hybrid convolutional networks like hybrid of Xception and mobileNetV2. But if we compare our ensemble model with individual swin transformer, then also our proposed model gives better result. Experiments were conducted on a dataset containing MRI images with multi-class tumors. The proposed ensemble SXM-Net for popular multi- categories of brain tumors achieved better test accuracy, with high average sensitivity values and high average specificity values, giving better experimental specific outcomes than individual traditional models.