A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Abstract

Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

“…Rehman et al [29] presented a framework for the diagnosis of 15 different forms of chest disease, including COVID-19, using a chest radiograph modality. They used a convolutional neural network (CNN) with a softmax classifier and a fully connected layer to extract deep features, which are input into traditional machine learning (ML) classification algorithms.…”

“…The ML algorithms have been shown to be very effective and robust algorithms that can handle large data successfully. Therefore, it can be used to analyze the epidemiology of COVID-19 [21][22][23][24][25][26][27][28][29].…”

COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network

“…Khan et al [ 32 ] proposed a contrast enhancement scheme by combining a top-hat and Wiener filter using parallel fusion and optimization of pre-trained deep learning frameworks of VGG16 and AlexNet to automatically extract and fuse features for COVID-19 screening using CT scans to obtain 98% accuracy. Rehman et al [ 33 ] presented a two-way classification technique using chest X-ray modality to diagnose 15 different forms of chest disorders, including the COVID-19 condition and achieved 99.98% accuracy.…”

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

“…Artificial intelligence (AI) techniques have been deployed to combat the epidemic caused by COVID-19 and its negative consequences [7], and, specifically, for medical diagnostics [8]. Utilizing deep learning (DL), a modern form of machine learning, this disease can be detected and identified at early stages from the X-ray and CT frames of the chest [9][10][11]. The most common diagnostic X-ray examination is the chest X-ray.…”