Artificial Intelligence-Based Brain Tumor Segmentation Using Adaptive Hybrid CNN and Classification by Multi-Scale Dilated MobileNet with Attention Mechanism for MRI Images

Abstract

In the medical field, the demanding task is predicting the type of cancer cell, and the analysis to reduce the death rate. MRI technique is used for capturing the brain image because of its high-quality performance and non-ionizing radiation. These collected input images carry only the area of tissues as bright and remove the cerebrospinal fluid to increase the quality of the image. The supervised learning method indicates marvelous performances in solving the problems related to the segmentation and detection of medical images. In the existing model, Convolutional Neural Network (CNN) is used, and the execution of CNN is carried out in different layers. This network consists of different layers that lead to various challenges, such as execution cost and time being high, fetching the wrong input, arising misdiagnosis of the disease. Prediction using different datasets is difficult and creates an overfitting problem, and large memory is needed in the existing method. To overcome this entire issue, an advanced model is developed. Input images collected from online sources are given to the Brain tumor segmentation stage, where the Adaptive Hybrid Convolutional Neural Network (AHCNN) is utilized. Here, the TransResUNet and Fully Convolutional Network (FCN) is utilized to construct the AHCNN for effectively segmenting brain tumors. Here, the parameters are optimized using Flock Member Movement-Based Hybrid Coati with Tomtit Flock Optimization (FMM-HCTFO). The TransResUNet and FCN outcomes are fed to the prediction stage. Here, the brain tumor classification is done using the Multi-scale Dilated Adaptive MobileNet with Attention Mechanism (MSDM-AM) technique. Here, also the parameters are optimized using FMM-HCTFO. Finally, the brain tumor will be classified using the MSDM-AM technique. The achievement of the developed model for the prediction of the brain is increased by tuning the variables using the proposed algorithms.