An approach for brain tumor detection using optimal feature selection and optimized deep belief network.

The proposed intelligent method for accurate brain tumor detection is composed of five phases, "(a) skull stripping (b) tumor segmentation (c) feature extraction (d) optimal feature selection and (e) classification". Initially, the input MRI image is collected from the Kaggle dataset. This MRI image is subjected to the first step called Pre-processing, which is done using the skull stripping and the entropy-based trilateral filtering methods. The skull stripping removes the non-cerebral tissues, and only the skull part is considered. The skull stripping is done using the Otsu thresholding. The entropy-based trilateral filtering smoothens the images by preserving the edges using a nonlinear combination of nearby pixel values. The graphicalarchitectural diagram of a proposed intelligent method for brain tumor detection has been shown in Graphical abstract. The pre-processed image is given to the tumor segmentation phase. Here the tumors are segmented from the image using fuzzy centroid-based region growing. Once the tumor segmentation is performed, it undergoes the feature extraction phase to extract the features. This is accomplished using four features such as GLCM, GLRM, statistical features, and shape features. Here, the statistical features extract the features from the first-order statistics such as, "mean, median, standard deviation, and variance", and higher-order statistics such as, "RMS, smoothness, kurtosis, and skewness". Similarly, the shape features extract the "area, eccentricity, extent, circularity, and Euler number" as features. For minimizing the complexity that occurs during the classification phase, it is mandatory to choose the most significant features from the long feature length. This is done in the optimal feature selection phase, in which the optimization is done by the hybrid meta-heuristic algorithm by integrating GSO and MVO. The resultant algorithm is termed the GS-MVO. These optimally selected features are subjected to the deep learning algorithm called DBN in the classification phase. An improvement is made in the DBN by optimizing its weight using the same GS-MVO. [Display omitted] • Undergoes pre-processing by the skull stripping and the entropy-based trilateral filtering methods for removing the non-cerebral tissues by the Otsu thresholding and smooth the images by preserving the edges. • Performs the tumor segmentation for separating the tumor from the normal brain tissues using the fuzzy centroid-based region growing. • Extracts the GLCM, GLRM, statistical, and the shape features from the tumor segmented image to more manageable groups for the processing. • Performs the optimal feature selection for choosing the most significant features to minimize the classification complexity by the proposed GS-MVO. • Classifies the MRI brain image as normal or abnormal by the deep learning algorithm called DBN, where the weight is optimized by the same proposed GS-MVO. Nowadays, a Magnetic Resonance Image (MRI) scan acts as an efficient tool for efficiently detecting the abnormal tissues present in the brain. It is a complex process for radiologists to diagnose as well as classify the tumor from several images. This paper develops an intelligent method for the accurate detection of brain tumors. Initially, the pre-processing is performed for the input MRI image using the skull stripping and the entropy-based trilateral filtering methods. Further, fuzzy centroid-based region growing is adopted for segmenting the tumor from the image. Once the tumor is segmented, feature extraction is done using four sets of well-performing features like Gray-Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRM), statistical features, and shape features. The optimal feature selection is performed by the hybrid meta -heuristic algorithm termed Group Search-based Multi-Verse optimization (GS-MVO). Finally, the optimally selected features are given to a deep learning algorithm called Deep Belief Network (DBN). The weight is optimized by the same GS-MVO that classifies the final image as normal or abnormal. The simulation outcomes are performed by the standard benchmark database which proves that the developed technique obtains a high classification accuracy. From the analysis, the accuracy of the proposed GS-MVO-DBN is 9.09% superior to SVM, 7.14% superior to NN, 3.45% superior to DBN, 17.65% superior to CNN, 15.38% superior to NN-CNN, and 1.69% superior to COR-CSO-CNN-NN. The proposed GS-MVO-DBN is very effective in accurately detecting brain tumors. In the future, it is encouraged to work on challenging parts of the tumor region like edema, necrosis, and active regions with the help of the fusion process of multi-modality MRI images and effective pre-processing techniques incorporated with innovative deep learning methods. [ABSTRACT FROM AUTHOR]