Innovative brain tumor detection using optimized deep learning techniques

• Published in: International journal of system assurance engineering and management Vol. 14; no. 1; pp. 459 - 473
• Main Authors: Ramtekkar, Praveen Kumar, Pandey, Anjana, Pawar, Mahesh Kumar
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.02.2023; Springer Nature B.V
• Subjects: Accuracy; Ant colony optimization; Artificial intelligence; Artificial neural networks; Brain; Brain cancer; Brain research; Datasets; Deep learning; Engineering; Engineering Economics; Feature extraction; Genetic algorithms; Image processing; Image segmentation; Logistics; Machine learning; Magnetic resonance imaging; Marketing; Medical imaging; Neuroimaging; Optimization algorithms; Optimization techniques; Organization; Original Article; Particle swarm optimization; Preprocessing; Programming languages; Python; Quality Control; Reliability; Safety and Risk; Search algorithms; Semantic web; Swarm intelligence; Tomography; Tumors; CNN; MRI; WOA; GA; ACO; PSO; GLCM; GWO; BCO
• ISSN: 0975-6809; 0976-4348
• DOI: 10.1007/s13198-022-01819-7

An unusual increase of nerves inside the brain, which disturbs the actual working of the brain, is called a brain tumor. It has led to the death of lots of lives. To save people from this disease timely detection and the right cure is the need of time. Finding tumor-affected cells in the human brain is a cumbersome and time- consuming task. However, the accuracy and time required to detect brain tumors is a big challenge in the arena of image processing. This research paper proposes an innovative, accurate and optimized system to detect brain tumors. The system follows the activities like, preprocessing, segmentation, feature extraction, optimization and detection. The preprocessing system uses a compound filter, which is a composition of Gaussian, mean and median filters. Threshold and histogram techniques are applied for image segmentation. Grey level co- occurrence matrix is used for feature extraction. The optimized convolution neural network (CNN) technique is applied here that uses ant colony optimization, bee colony optimization and particle swarm optimization, genetic algorithm, gray wolf optimization and whale optimization algorithm techniques for best feature selection. Detection of brain tumors is achieved through CNN classifiers. This system compares its performance with another modern technique of optimization by using accuracy, precision and recall parameters and claims the supremacy of this work. This system is implemented in the Python programming language. The brain tumor detection accuracy of this optimized system has been measured at 98.9%.