A hybrid meta-heuristic approach for brain abnormalities detection using CNN Deep Learning Network

Abstract

Brain tumor is the most common brain abnormalities in children and adults. Brain tumors are the reason for one-third of all cancer deaths in the world. Image processing techniques and algorithms help a lot to perform this research and presented a second idea for analysis improvement and accuracy detection of radiologists. Deep Learning (DL) has achieved a huge number of gaps in various image processing and computer vision problems such as classification, segmentation, excellent resolution and so on. CNNs have been used in the field of computer vision for decades. However, the use of conventional CNNs has shown significant performance, there is still a lot to do for improvement. Like most artificial neural networks, CNN is prone to multiple local optimum states. In order to avoid trapping in the local optimum state, local optimization algorithms are required. In this paper, sine-cosine algorithm (SCA) and artificial bee colony (ABC) methods, two well-known metaheuristic algorithms, are proposed as an alternative approach to optimize CNN performance and they are also applied for image segmentation in order to detect brain anomalies. The simulation results of the proposed method show that the accuracy of the proposed method has improved by 5% compared to the base paper. This is due to the optimal selection of CNN parameters.