A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction

Abstract

This review aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. A systematic literature review of empirical studies was conducted on the ML models published in the last decades. Fifty-one primary studies relevant to the objective of this research were revealed. After investigating these studies, five ML techniques have been employed in brain tumor classification and prediction. Ultimately, the estimation accuracy of these ML models could be regarded and accepted and outperformed non-ML models. ML models have been revealed to be useful in brain tumor classification and prediction. Genetic algorithm among the ML models achieved an accuracy of 100%. Nevertheless, ML models are still restricted in the industry, so initiative and encouragement are needed to make ML models easier. Further work is required on these ML models to verify the accuracy and consider other performance metrics other than the accuracy.

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

“…Inspired by [ 27 , 28 , 29 ], we ranged the settings of the percentages of the Gaussian noise as the noise insertion into images from 5 to 50%, with a step size of 5%. The percentage specifies the ratio of the standard deviation of the Gaussian noise versus the signal of the entire image.…”

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

“…The main advantage of a single channel is high efficiency, while the main advantage of multiple channels is multidimensional, comprehensive, and high recognition rate. From the perspective of feature extraction, it can be divided into manual feature extraction methods and deep learning-based automatic feature extraction methods (Ghosh et al, 2021;Appati et al, 2021). The manual feature extraction approach has rich prior knowledge and can fully utilize non-stationary non-linear EEG signals to achieve feature extraction.…”