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•A Pixel Increase along Limit (PIaL) based enhancement method is proposed.•Tumor segmentation is performed through saliency based deep learning.•PSO optimization and entropy padding based active features selection.•A features based extensive comparison is conducted.
Glioma is a kind of brain tumor that can arise at a distinct location along with dissimilar appearance and size. The high-grade glioma (HGG) is a serious kind of cancer when compare to low-graded glioma (LGG). The manual diagnosis process of these tumors is tiring and time consuming. Therefore, in clinical practices, MRI is useful to assess gliomas as it provides essential information of tumor regions. In this manuscript, an active deep learning-based feature selection approach is suggested to segment and recognize brain tumors. Contrast enhancement is made in the primary step and supplied to SbDL for saliency map construction, which later converts into binarized form by applying simple thresholding. In the classification phase, the Inception V3 pre-trained CNN model is employed for deep feature extraction. These features are simply concatenated along with dominant rotated LBP (DRLBP) for better texture analysis. Later, the concatenated vector is optimized through particle swarm optimization (PSO), so as to classify using softmax classifier. The experiments are conducted in two phases. At first, the segmentation approach SbDL is validated on BRATS2017 and BRATS2018 datasets. The achieved dice score for the BRAST2017 dataset is 83.73% for core tumor, 93.7% for the whole tumor and 79.94% for enhanced tumor. For BRATS2018 dataset, dice score obtained is 88.34% (core), 91.2% (whole) and 81.84% (enhanced). At the second, the classification strategy is applied on BRATS2013, 2014, 2017 and 2018 with an average accuracy of more than 92%. The overall results show that the presented method outperforms for both segmentation and classification of brain tumors.