# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 2 Related Work  
The healthcare domain has witnessed significant advancements in image processing, machine learning (ML), deep learning (DL), and computer vision over the past two decades. The outbreak of the COVID-19 pandemic further accelerated research in these fields, leading to a surge in studies related to COVID-19 detection and diagnosis using medical imaging [1; 9; 10; 11; 12; 13]. During the past decades, many healthcare diagnostic analyses, including X-ray, CT scan, or MRI research, were confined to image processing [14; 15; 16; 17; 18; 19; 20; 21; 22] and ML [9]. Recent developments in DL, especially Convolutional Neural Networks (CNNs) [23; 24; 25; 26], have shown promising results in various medical imaging tasks, including COVID-19 detection from chest X-ray images [27; 28; 29; 30; 31]. Authors in [26] aimed to classify and detect objects in X-ray baggage imagery using Convolutional Neural Networks (CNNs) with support vector machines (SVM). This study highlighted that CNN-based feature extraction outperformed handcrafted Bag-of-Visual-Words features. The identification of anomalies in chest X-rays, including the detection of the COVID-19 virus, is presented in [1]. A framework for early-phase identification of COVID-19 using chest X-ray imaging features is developed in [9]; this research offered an innovative approach to early COVID-19 detection using machine learning. A comprehensive overview of diagnostic methods for COVID-19, including CT scans, NAAT, RT-PCR, and CRISPR is introduced in [27]. In the domain of deep learning for medical imaging, [PERSON] et al. [25] explored the potential of deep learning in COVID-19 detection, utilizing CNNs and SVMs. Interestingly, their study compared local descriptors with deep learning, noting that while local descriptors were effective, they consumed more time. The authors of [24] investigated the use of stationary wavelets for data augmentation and abnormality detection in CT scans. Their study proposed a three-phase method for detecting abnormalities in lung CT scans, classifying them into COVID-positive and COVID-negative cases. In addition, a comparative analysis of deep learning models,including Inception V3, Xception, and ResNeXt, for COVID-19 detection based on chest X-rays is presented in [23]. In response to the urgent need for effective COVID-19 diagnostic tools, COVID-Net, a deep Convolutional Neural Network designed for detecting COVID-19 cases from chest X-ray images, was introduced in [32]. Another knowledge transfer and distillation (KTD) framework using CNNs for on-device COVID-19 patient triage and follow-up is proposed in [33]. Finally, a deep learning-based methodology for detecting coronavirus-infected patients using X-ray images was developed in [34]. Despite the progress, challenges remain in automated COVID-19 detection, such as text in chest X-rays, dimension mismatch, data imbalance, and the need for large and diverse datasets. In this research, we propose a novel DL-based COVID-19 diagnostic method using state-of-the-art architectures. We address the challenges of preprocessing chest X-ray images from various sources to build a robust classification model. This study introduces innovative methods in image preprocessing to enhance the accuracy of COVID-19 detection. Specifically, we employ the Canny edge detection algorithm, a technique widely used in computer vision, to effectively reduce noise and emphasize critical edges within X-ray images. This process results in a more refined and informative image representation. Following the Canny edge filter, we apply the Gradient-weighted Class Activation Mapping (Grad-CAM) method. This approach offers significant advantages by highlighting essential regions within the X-ray image. The Grad-CAM-generated map serves as an intermediary step that empowers the neural network to identify infected areas with greater precision, ultimately boosting the accuracy of COVID-19 detection.