Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm. (20th November 2019)

• Record Type: Journal Article
• Authors: Gong, Shu; Gao, Wei; Abza, Francis
• Is Part Of: Computational intelligence. Volume 36:Number 1(2020)
• Page Start: 259
• Page End: 275
• Publication Date: 2019-11-20
• Subjects: artificial neural network -- brain tumor -- feature classification -- tumor detection -- whale optimization algorithm; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12259
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.