Abstract
Brain tumour identification, segmentation cataloguing from MRI images is most thought-provoking and is a very much essential for many medical image analysis applications. Every brain imaging modality provides information about various parts of the tumor. In current years deep learning systems have shown auspicious outcomes in medical image investigation tasks. Despite several recent works achieved a significant result on brain tumour segmentation and classification, they come with an improved performance at the expense of increased computational complexity to train and test the system. This exploration paper investigates the efficacy of popular deep learning architectures namely Xception Net, MobileNet for classification and DeepLab for segmentation of the cancerous region of brain tumor. Each architecture is trained using a BRATS 2018 dataset and evaluated for its performance in accurately classifying tumor presence and delineating tumor boundaries. The DeepLab models accomplished a best segmentation result with Pearson Correlation Coefficient values 0.50 respectively and the deep learning models Xception Net and MobileNet achieved an accuracy of 0.8921 and 0.9176respectively. The experimental results show that these architectures achieve high accuracy and precise segmentation. The findings of this study contribute to advancing the field of medical image analysis and hold implications for improving the analysis and dealing of brain tumors.