Detection of COVID-19 Cases from Chest X-Rays using Deep Learning Feature Extractor and Multilevel Voting Classifier

Abstract

Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.

Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection

“…Related works [13][14][15][16] only reported the results after applying the transfer learning model. Te percentage improvement of the accuracy is 7.80% [9], 3.77% [10], 1.34% [11], 5.88% [12], and 6.85-9.92% (our work).…”

“…(i) Source domain and target domain: the related works [9][10][11][12] formulated the transfer learning problem using a similar source domain and target domain whereas other works [13][14][15][16] considered the distant source and target domains. Our work considered 10 benchmark datasets to evaluate the MTL using similar and distant sources and target domains.…”

“…Related Works. Although existing works [9][10][11][12][13][14][15][16] formulated the transfer learning problems with a single source domain and single target domain, the discussion has merit as these works fell into the same research area, i.e., transfer learning for LCD. In the following, two common types of formulations will be discussed: (i) transfer learning between the similar source domain and target domain [9][10][11][12] and (ii) transfer learning between the distant source domain and target domain [13][14][15][16].…”

“…Te ablation study showed that the transfer learning strategy enhanced the accuracy of the LCD model from 79.5% to 85.7%. ImageNet was served as the source model in the transfer learning strategy to fne-tune the target model [10]. VGG16 and deep neural network were used to build the LCD model, which was evaluated using two benchmark datasets.…”

A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment

“…Once attackers gain access, they can manipulate or control the devices, potentially causing disruptions or unauthorized actions.Phishing attacks can also lead to the theft of sensitive data from IoT devices. By tricking users into providing their personal or financial information, attackers can gain access to valuable data stored on IoT devices, such as health records, financial information, or personal preferences [19,20]. This can have serious consequences for individuals and organizations, as the compromised data can be used for identity theft, fraud, or other malicious purposes.Furthermore, phishing attacks can exploit vulnerabilities in the IoT ecosystem.…”