Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images

Genetic Algorithm (GA) is one of the bio-inspired optimization techniques available for practical applications. The increasing necessity for bio-inspired optimization techniques has lead to the development of many innovative optimization techniques. In the backdrop, GA is completely forgotten and rarely used for practical applications. One of the significant reasons for the low preference of GA is the excessive randomness associated with this algorithm. The random nature of many processing steps in GA often leads to inaccurate results. The main focus of this research work is to enhance the usage of genetic algorithm for practical applications. Modified GA approaches are used in this work to overcome the drawback of the conventional approaches. In this research work, suitable modifications are made in the existing GA to minimize the random nature of conventional GA. Specifically, the focus of this work is to develop modified reproduction operators which forms the core part of this algorithm. Different binary operations are employed in this work to generate offspring in the process of crossover and mutation process. These binary operations are designed with a specific objective unlike the conventional binary operations in GA which are highly random in nature. The application of these approaches is explored in the context of medical image classification. Abnormal brain images from four different classes are used in this work. The proposed method has yielded 98% accuracy in comparison to other methods. Experimental results show promising results for the proposed approaches in terms of accuracy measures. (C) 2018 Elsevier B.V. All rights reserved.