Automatic Brain Tumor Detection in Brain MRI Images using Deep Learning Methods

Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the cancer image archive database included ۱۵۹ patients. First, two filters called median and Gaussian filters were used to improve the quality of the images. An edge detection operator is also used to identify the edges of the image. Second, the proposed network was first trained with the original images of the database, then with Gaussian filtered and Median filtered images. Finally, accuracy, specificity and sensitivity criteria have been used to evaluate the results. Proposed method in this study was lead to ۸۷.۲۱%, ۹۰.۳۵% and ۹۳.۸۶% accuracy for original, Gaussian filtered and Median filtered images. Also, the sensitivity and specificity was calculated ۸۲.۳% and ۸۴.۳% for the original images, respectively. Sensitivity for Gaussian and Median filtered images was calculated ۹۰.۸% and ۹۱.۵۷%, respectively and specificity was calculated ۹۳.۰۱% and ۹۳.۳۶%, respectively. As a conclusion, image processing approaches in preprocessing stage should be investigated to improve the performance of deep learning networks.

Keywords:

deep learning , Image Processing , Automatic Detection , Brain Tumor , MRI