Automated brain tumor detection and segmentation using modified UNet and ResNet model

The early automatic detection of brain tumors in MRI scans is a challenging endeavor due to the high resolution of the images. For a very long time, continual research efforts have been floating a new notion of substituting various grayscale anatomic parts of diagnostic pictures with suitable colors. If successful, this would be an effective way for radiologists to circumvent the challenges they now encounter. The coloring of grayscale photos is a complex process that aims to improve the contrast of different sections of an image by changing grayscale images into color pictures with high levels of contrast. It is common for the predictions to be lacking in fine detail when simply a U-Net design is used; to assist alleviate this issue, cross or skip connections may be introduced between the blocks of the network. Instead of creating a skip connection every two convolutions as it now is in a ResBlock, the skip connections cross from a portion of the same size in the downsampling route to a part in the upsampling path. This improves the overall accuracy of the model and performs better when compared to traditional UNet model.