An Efficient Classification of Multiclass Brain Tumor Image Using Hybrid Artificial Intelligence with Honey Bee Optimization and Probabilistic U-RSNet

Abstract

The life of the human beings are considered as the most precious and the average life time has reduced from 75 to 50 age over the past two decades. This reduction of average life time is due to various health hazards namely cancer and many more. The brain tumor ranks among the top ten most common source of demise. Although brain tumors are not the leading cause of death globally, 40% of other cancers (such as breast or lung cancers) metastasize to the brain and become brain tumors. Despite being the gold norm for tumor diagnosis, a biopsy has a number of drawbacks, including inferior sensitivity/specificity, and menace when performing the biopsy, and lengthy wait times for the results. This work employs artificial intelligence integrated with the honey bee optimization (HBO) in detecting the brain tumor with high level of execution in terms of accuracy, recall, precision, F1 score and Jaccard index when compared to the deep learning algorithms of long short term memory networks (LSTM), convolutional neural networks, generative adversarial networks, recurrent neural networks, and deep belief networks. In this work, to enhance the level of prediction, the image segmentation methodology is performed by the probabilistic U-RSNet. This work is analyzed employing the BraTS 2020, BraTS 2021, and OASIS dataset for the vital parameters like accuracy, precision, recall, F1 score, Jaccard index and PPV.