Abstract:
Deep learning frameworks have proven to be a powerful tool for disease detection and classification, allowing for accurate predictions and earlier interventions, particularly in the case of viral diseases. Chest X-ray is a primary imaging modality that plays a crucial role in the diagnosis of Covid-19 dis-ease. While convolutional neural networks (CNNs) have been successful in image recognition and classification due to the availability of large-scale annotated image datasets, medical image classification remains a significant challenge due to the limited availability of annotated medical images. This study presents a deep learning model for Covid-19 diagnosis from chest X-rays. The study uses two distinct Chest X-Ray datasets from two different sources to train and test the model. The model is developed using a CNN network and the proposed model accurately classifies chest X-rays into positive and negative categories, providing an automated and efficient method for the viral disease diagnosis which is crucial in the control of the ongoing pandemic and toward a safer future.