A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification. (May 2022)

• Record Type: Journal Article
• Authors: Demir, Fatih; Akbulut, Yaman
• Is Part Of: Biomedical signal processing and control. Volume 75(2022)
• Publication Date: 2022-05
• Subjects: R-CNN -- Deep features -- L1NSR feature selection -- MRI -- Brain tumor detection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103625
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A robust technique is presented for MR image-based brain tumor detection. The residual blocks improve the classification achievement. Deep features are extracted from FC layers and residual-convolutional layers. The L1NSR feature selection algorithm improves the classification performance. Abstract: One of the most dangerous diseases in the world is brain tumors. After the brain tumor destroys healthy tissues in the brain, it multiplies abnormally, causing an increase in the internal pressure in the skull. If this condition is not diagnosed early, it can lead to death. Magnetic Resonance Imaging (MRI) is a diagnostic method frequently used in soft tissues with successful results. This study presented a new deep learning-based approach, which automatically detects brain tumors using Magnetic Resonance (MR) images. Convolutional and fully connected layers of a new Residual-CNN (R-CNN) model trained from scratch were used to extract deep features from MR images. The representation power of the deep feature set was increased with the features extracted from all convolutional layers. Among the deep features extracted, the 100 features with the highest distinctiveness were selected with a new multi-level feature selection algorithm named L1NSR. The best performance in the classification stage was obtained by using the SVM algorithm with the Gaussian kernel. The proposed approach was evaluated on two separate data sets composed of 2-class (healthy and tumor) and 4-class (glioma tumor, meningioma tumor, pituitary tumor, and healthy) datasets. Besides, the proposed approach was compared with other state-of-the-art approaches using the respective datasets. The best classification accuracies for 2-class and 4-class datasets were 98.8% and 96.6%, respectively.