Abstract
Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.
Highlights
A brain tumour is an abnormal cell development in the human brain
It is shown that the maximum value of the train size and the number of epochs and the lowest value of the learning rate gave the minimum value of the output response, i.e., the training loss in the proposed convolutional neural network (CNN) model
We provide three metaheuristic optimization approaches that were inspired by natural phenomena: the sunflower optimization algorithm, the forensic-based investigation algorithm and the material generation algorithm
Summary
A brain tumour is an abnormal cell development in the human brain. Many distinct forms of brain tumours occur in diverse areas of the globe. Zhao et al [6] established a strategy for integrating fully conventional neural networks (FCNNs) and conditional random fields (CRFs) for detecting the segmentation of brain tumours by utilising 2D image patches and slices They utilized picture data from BRATS 2013, 2015 and 2016 for their experiments. Dong et al [33] established a completely automated system for segmenting brain tumours using deep convolutional networks based on U-Net. A technique was built by Padole and Chaudhari [34] for identifying the brain tumours in MRI pictures through part examination where normalized cut (Ncut) and mean shift algorithms were combined to identify the cerebrum tumour surface zone naturally. CNN, Section 3 explains the different optimization approaches for training loss, Section 4 compares the result of the optimization approaches with the learning rate and, Section 5 provides the conclusion derived from the study output and outlines the scope of its potential development
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