Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Abstract

The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

“…This shows that features extracted from CNN pre-trained models can be used to build good ML models with satisfactory accuracy. Figure 11 also shows that the RNN constructed by Kaur et al [ 94 ] generated the second-best result for ML-based brain tumor classification. In this study, ICA was utilized for feature extraction, while a combination of the LO and BO algorithms was utilized for image smoothing.…”

“…Kaur et al [ 94 ] introduced a brain tumor classification technique using multiple metaheuristic and ML algorithms. In the study, brain MRI was pre-processed, and features were extracted from the images using Principal Component Analysis (PCA) and Independent Component Analysis (ICA).…”

“…The SVM model designed by Sekhar et al [ 14 ] produced the best performance for ML-based tumor classification. The RNN constructed by Kaur et al [ 94 ] generated the second-best result for ML-based brain tumor classification. The remaining competitors include the DenseNet-169 proposed in Kang et al [ 84 ], the ensemble approach exploited by Jena et al [ 15 ], and the work of Deepak and Ameer [ 13 ] that combined SVM with CNN.…”

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

“…The features were selected using an optimizer, and the ensemble classifier helped in achieving higher accuracies by reducing the medical data [ 103 ]. The researchers in [ 163 ] applied various metaheuristic techniques such as firefly, cuckoo, Bat, and Lion optimizers. The hybridization of Lion and Bat optimization obtained the highest accuracy among others.…”

An Enhanced Machine Learning Approach for Brain MRI Classification

“…They utilized only 70 images for their corresponding experiments. In contrast, a cutting-edge system with three fundamental modules was created by [ 12 ] for the identification of brain tumors. They utilized histogram equalization in the pre-processing module in order to enhance the contrast of the brain MRI images.…”