Deep learning for COVID-19 diagnosis based on chest X-ray images

Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance

Deep learning-based cancer classification for microarray data: a systematic review

Deep neural networks are robust techniques and recently used extensively for building cancer classification models from different types of data. Nowadays, microarray gene expression datasets consider an essential source of data that is used in cancer classifications. However, due to the small size of samples compared to the high dimensionality of microarray data, many machine learning techniques have failed to distinguish the most relevant and informatics genes. Therefore, deep learning is demand due to its ability to automatically discovering the complex relationship between features with significant accuracy and high performance. The current study aims to reveal the state-of-the-art of deep neural network architectures and how it can utilize from microarray data. Therefore, several deep neural network architectures were built such as CNN, DNN, RNN, DBN, DBM and DAE to be compatible with the different learning processes (supervised, unsupervised and semi-supervised). As a result, CNN considers the most common neural network architecture used in the medical field due to its robustness and high performance in cancer classification. Results indicate that choosing suitable architecture of the deep neural network and its hyperparameters is one of the most difficulties facing the researcher in designing models for cancer prediction and classification because there is no particular rule to ensure high prediction accuracy

Memory based cuckoo search algorithm for feature selection of gene expression dataset

Cancer prediction has been shown to be important in the cancer research area. This importance has prompted many researchers to review machine learning-approaches to predict cancer outcome using gene expression dataset. This dataset consists of many genes (features) which can mislead the prediction ability of the machine learning methods, as some features may lead to confusion or inaccurate classification. Since finding the most informative genes for cancer prediction is challenging, feature selection techniques are recommended to pick important and relevant features out of large and complex datasets. In this research, we propose the Cuckoo search method as a feature selection algorithm, guided by the memory-based mechanism to save the most informative features that are identified by the best solutions. The purpose of the memory is to keep track of the selected features at every iteration and find the features that enhance classification accuracy. The suggested algorithm has been contrasted with the original algorithm using microarray datasets and the proposed algorithm has been shown to produce good results as compared to original and contemporary algorithms