Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

• Published in: Electromagnetic biology and medicine Vol. 43; no. 1-2; p. 31
• Main Authors: S, Senthil Pandi, A, Senthilselvi, T, Kumaragurubaran, S, Dhanasekaran
• Format: Journal Article
• Language: English
• Published: England 02.04.2024
• Subjects: Algorithms; Brain Neoplasms - classification; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Color; Deep Learning; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; Neural Networks, Computer; Colour harmony algorithm; improved non-subsampled shearlet transform; BRATS dataset; mean curvature flow based pre-processing method; brain MR Images; Brain cancer
• ISSN: 1536-8386
• DOI: 10.1080/15368378.2024.2312363

This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.