# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies  
In various fields, such as malware detection, medicine, information retrieval, and many others, the use of DL models has seen rapid growth. Recently, a new DL methodology based on Convolutional Neural Networks (CNNs) [35; 36] has shown significant promise in various image analysis tasks. DL algorithms enable computational models to learn data representations across multiple abstraction layers, leading to improved performance in classification tasks. DL models have demonstrated higher accuracy and have the potential to enhance human performance in various domains [15; 37; 38; 39; 40; 41; 42; 43].  
Our proposed CNN-based COVID-19 detection method consists of a sequence of phases, as depicted in Figure 1.  
The following five phases summarize the COVID-19 detection process:  
1. **Data Collection and Pre-processing:** In this phase, a dataset containing chest X-ray scans of COVID-19 patients and healthy individuals is collected. The images may contain noise and artifacts, and thus, pre-processing techniques are applied to enhance the quality of the images. Additionally, Canny edge detection is employed to identify edges in the X-ray images, which can help in feature extraction. In this study, we utilized a dataset sourced from the Kaggle repository [44], a recognized platform for medical image datasets. The dataset consists of 6432 chest X-ray images, including cases of COVID-19, normal chest X-rays, and pneumonia. To ensure a balanced representation, we divided the dataset into training and validation sets. The training set comprises 1345 normal cases, 490 COVID-19 cases, and 3632 pneumonia cases, while the validation set includes 238 normal cases, 86 COVID-19 cases, and 641 pneumonia cases. We downscaled the PA (posteroanterior) view scans of COVID-19-affected patients to \(128\times 128\) pixels to optimize model training and prevent overfitting.
2. **Feature Extraction with Gradient-weighted Class Activation Mapping (Grad-CAM):** Grad-CAM is a powerful technique that highlights the regions of the image that are crucial for the model's prediction. By applying Grad-CAM, we can visualize the regions in the chest X-ray that contribute the most to the COVID-19 detection decision. This not only helps in understanding the model's decision-making process but also provides insights into relevant areas of the X-ray for COVID-19 identification.
3. **Data Splitting:** The original dataset is divided into two sets: a training set and a validation set. The training set is used to train the CNN model, while the validation set is used to assess the model's performance and fine-tune hyperparameters.
4. **Training the CNN Model:** The CNN model is trained on the training dataset using the extracted features and Grad-CAM visualizations. During training, the model  
Figure 1: Block diagram of proposed method.  
learns to identify patterns and features that distinguish COVID-19 cases from healthy cases in the X-ray images.
5. **Performance Evaluation:** The trained CNN model's performance is evaluated on the validation dataset to measure its accuracy and other relevant metrics. This evaluation helps determine the model's effectiveness in detecting COVID-19 cases from chest X-ray images.  
The proposed method aims to leverage the power of CNNs and Grad-CAM visualization to improve the accuracy and interpretability of COVID-19 detection from chest X-ray images. By following these phases, our approach aims to provide an efficient and reliable tool for assisting medical professionals in screening and diagnosing COVID-19 cases.