Application of novel DIRF feature selection algorithm for automated brain disease detection

Abstract

The brain is a complex organ and Magnetic Resonance Imaging (MRI) is the most widely used imaging modality for diagnosing brain diseases due to its superior soft tissue contrast. In clinical practice, interpreting MRI scans is laborious, time-consuming, and error-prone. While most studies focus on detecting brain tumors and multiple sclerosis, there are limited studies on atrophy, ischemia, and white matter intensity (WMI) diseases. Moreover, most studies are complex, time-consuming, computationally intensive, and specific to the detection of a particular brain disease. Our study presents a new deep learning-based ensemble model and also introduces the novel double iterative ReliefF (DIRF) feature selection algorithm to enhance the performance of automatic detection of atrophy, ischemia, and WMI using MRI images. Two pre-trained deep models, namely AlexNet and GoogleNet, and three well-known local texture descriptors, namely pyramid histogram of oriented gradients (PHOG), binary Gabor pattern (BGP), and binarized statistical image features (BSIF), were employed as feature extractors, and the extracted features were merged. The merged features were fed into the proposed double iterative ReliefF (DIRF) feature selection algorithm, and the most important 688 features were selected. Finally, the selected features were classified by support vector machine (SVM). Our proposed DIRF-based ensemble model achieved 98.87 % accuracy using a 10-fold cross-validation strategy. Our proposed model developed using a public brain diseases MRI dataset achieved an accuracy of 95.96 %. This study demonstrates that our proposed model, with DIRF feature selection algorithm can significantly improve the diagnosis of various brain diseases, enhancing the patient care and outcomes.