Hybrid fruit bee optimization algorithm-based deep convolution neural network for brain tumour classification using MRI images.

Abstract

An accurate classification of brain tumour disease is an important function in diagnosing cancer disease. Several deep learning (DL) methods have been used to identify and categorize the tumour illness. Nevertheless, the better categorized result was not consistently obtained by the traditional DL procedures. Therefore, a superior answer to this problem is offered by the optimized DL approaches. Here, the brain tumour categorization (BTC) is done using the devised Hybrid Fruit Bee Optimization based Deep Convolution Neural Network (HFBO-based DCNN). Here, the noise in the image is removed through pre-processing using a Gaussian filter. Next, the feature extraction process is done using the SegNet and this helps to extract the relevant data from the input image. Then, the feature selection is done with the help of the HFBO algorithm. Additionally, the brain tumour classification is done by the Deep CNN, and the established HFBO algorithm is used to train the weight. The devised model is analysed using the testing accuracy, sensitivity, and specificity and produced the values of 0.926, 0.926, and 0.931, respectively.