Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images

Abstract

The detection of Brain cancer is an essential process, which is based on the clinician’s knowledge and experience. An automatic tumor classification model is important to handle radiologists to detect the brain tumors. However, the precision of present model should be enhanced for appropriate treatments. Numerous computer-aided diagnosis (CAD) models are offered in the literary works of medical imaging to help radiologists concerning their patients. This paper proposes an optimization-driven technique, namely Whale Harris Hawks optimization (WHHO) for brain tumor detection using MR images. Here, segmentation is performed using cellular automata and rough set theory. In addition, the features are extracted from the segments, which include tumor size, Local Optical Oriented Pattern (LOOP), Mean, Variance, and Kurtosis. In addition, the brain tumor detection is carried out using deep convolutional neural network (DeepCNN), wherein the training is performed using proposed WHHO. The proposed WHHO is designed by integrating Whale optimization algorithm (WOA) and Harris hawks optimization (HHO) algorithm. The proposed WHHO-based DeepCNN outperformed other methods with maximal accuracy of 0.816, maximal specificity of 0.791, and maximal sensitivity of 0.974, respectively.

Keywords

• Brain tumor detection
• MR images
• Cellular automata
• Deep convolution neural network
• Rough set theory