Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm

• Published in: Healthcare (Basel) Vol. 10; no. 12; p. 2340
• Main Authors: Samee, Nagwan Abdel, Ahmad, Tahir, Mahmoud, Noha F., Atteia, Ghada, Abdallah, Hanaa A., Rizwan, Atif
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.11.2022; MDPI
• Subjects: Algorithms; Artificial intelligence; Brain cancer; Brain research; Brain tumors; Classification; Clinical decision making; Data mining; Decision support systems; Deep learning; Diagnosis; Glioma; Gliomas; Image segmentation; Literature reviews; Machine learning; Magnetic resonance imaging; Medical imaging equipment; Methods; Neural networks; Patients; Precision medicine; Tumors; Saudi Arabia; CNN; CAD system; U-Net; clinical decision; brain tumor; segmentation; classification
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare10122340

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN’s proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%.