Volume 20 No 22 (2022)
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Brain Tumor Detection and Classification on MRI using Variable wise Weighted Stack Auto-Encoder and Social Sky Driver Optimization Algorithm
Champakamala S, Karunakara K,
Nowadays, the medical technology named MRI (Medical Resonance Imaging) is extensively used for the detection of tumor (glioma) and in the diagnosis of different types of tissue abnormalities. Automatic segmentation and classification procedures from medical images are very important for earlier treatment planning and clinical assessment of brain tumors. The advancements in the field of computerized medical imaging play a major role in systematic research and support the doctors to offer essential treatments to patients with quick decision making. This work focus on the efficient segmentation and classification using deep learning (DL) models motivated by diagnosing tumor growth and treatment processes. The integration of the following techniques such as pre-processing, segmentation, extraction, selection and classification are used in this work to detect the brain tumor. Initially, pre-processing is done to improve the quality of image. Then segmentation is done using neutrosophic set expert maximum fuzzy sure entropy (NSEMFSE) with OTSU method. Next step is feature extraction, which make use of GLCM (Gray level co-occurrence matrix), SIFT (Scale-Invariant Feature Transform)descriptor and BoW(Bag of Words) techniques. Harris hawks optimization (HHO) algorithm is used for feature selection. Finally, the brain tumor is classified as benign and malignant using a VWSE (variable-wise weighted stack auto-encoder) and for the further classification of the malignant tumor as low, medium and high using social ski-driver (SSD) optimization algorithm. Simulation is performed in PYTHON platform. The dataset used is BRATS 2020. The performance of the proposed method is measured in terms of accuracy, precision, recall and F1 score.
Brain Tumor, BRATS 2020, Deep Neural Network, Segmentation, Medical Imag
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