An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

Abstract

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients’ benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System

“…DWT + PCA + kNN [18] 83.87 RT + PCA + LS-SVM (RBF) [20] 86.02 DWPT + GEPSVM [21] 88.92 DWT + PCA + LS-SVM (RBF) [6] 89.25 DWT + PCA + RF this paper 95.70…”

“…For a comprehensive comparison of decision models' performance on the multi-classification of brain MRIs, the proposed research compared five different decision models (J48, kNN, RF, and LS-SVM with polynomial (Poly) and radial basis functions (RBF)). For comparative analysis with the proposed system, some of the other published methods from recent literature [6,18,20,21] were also tested using the same large datasets.…”

“…To improve the efficiency of a classifier, the master features in the MRI image is needed to be identified properly. In recent literature [6,[14][15][16][17][18][19][20][21][22][24][25][26][27], there are many different algorithms (such as DWT and Ripplet transform) used to extract the main features of the images. DWT has some advantages over RT, being less computationally complex and also due to the characteristics of brain MRIs.…”

“…SVM plays a vital role in the application of object detection [42], face detection [43], handwriting recognition [44], medical imaging classification [6], and bioinformatics [45]. SVM learns from training examples.…”

“…Initially, these methods [6,18,20,21] were proposed for binary classification of brain MR images. The average accuracy results (when using 20 principal features) were gathered for each scheme, as presented in Table 3.…”