Brain Tumour Segmentation Using JeisloNet - a Unet Architecture

— Manual medical images segmentation is very tedious task. Accurate segmentation of brain magnetic resonance images (MRI) is a critical phase in measuring the irregularities in brain structure. In recent years, deep learning has gained popularity for its efficiency in brain image segmentation. In particular, Unet architecture is being deployed in several biomedical fields for segmentation. It has contributed immensely to solving clinical problems, which includes accurate segmentation of desired feature, efficient processing, and analysis of biomedical images, therefore enhancing an improved accuracy in biomedical images diagnosis and prognosis. In this research, JesloNet, a Unet architecture was developed for the automatic segmentation of abnormal tissues, brain tumours in MR scan images. MR scanned brain images were obtained from The Cancer Image Archives, the images were preprocessed and split in train and test set in the ratio 80:20. The experiment results showed that the Unet model, JeisloNet achieved good performance with the following results in Dice Coefficient Index (DSC) 0.9931, Mean IOU 0.9321, Global Accuracy 0.9928, and Error rate 0.0072. The result was also compared with other methods in the literature an JeisloNet performed better, hence, it can be adopted for other medical image segmentation