Convolutional Neural Network Based Medical Image Classifier

Deep learning had provided good outcome in analyzing images of tumours, however, the deficiency of large annotated datasets reduces its importance. The proposed medical image processing system is based on image segmentation and image classification. It is to be used by medical field experts. In order to classify the Brain tumour images the semantic level classification and segmentation network techniques are applied. This includes prior knowledge of testing samples and training samples, using Convolutional Neural Networks (CNN). The CNN based classifier improves the detection accuracy compared to the existing segmentation based classifier. In this project, the automated system would help the medical image analyst to identify the Brain Tumour in patient by making use of deep convolutional neural network (CNN).The image is obtained from MRI scan of a brain. The tumourless patient’s image dataset is used as the training and testing data for the classification network. Patient image is compared with dataset of a tumour affected images for differentiating an image as Non-timorous sample, low grade glioma and glioblastoma after segmenting and classifying the image. The Watershed segmentation algorithm is used for segmenting images and CNN is used for classifying the images. Finally the system will detect the tumour is affected or not in the given image of a patient’s brain, then the system will identify the tumour affected region and differentiate the low grade glioma and glioblastoma in the image.