An analysis comparing CT and X-ray images for COVID-19 identification employing deep learning and image processing methods

This study presents a comparative analysis of deep learning approaches for the identification of COVID-19 pneumonia in medical imaging, specifically focusing on CT and X-ray modalities. To aid healthcare professionals in quickly identifying infected individuals during the COVID-19 pandemic, accurate and efficient diagnostic tools are urgently required. In order to identify COVID-19 more quickly and accurately, deep learning algorithms combined with medical imaging data produced results. Computed Tomography (CT) and X-ray imaging are commonly used modalities for assessing lung involvement in COVID-19 patients, each offering unique advantages and challenges. Leveraging deep learning techniques, such as convolutional neural networks (CNNs), this analyzes the performance of models trained on large datasets of labeled CT and X-ray images. Our analysis includes preprocessing methods, model architectures, training procedures, and evaluation metrics to assess the efficacy of these approaches. Furthermore, this discusses the clinical implications, challenges, and ethical considerations associated with integrating deep learning-based diagnostic tools into routine clinical practice.