Published Paper Details:

Abstract: Covid-19 (SARC_CoV-2) instigated by a novel trait coronaviridae has become a universal health catastrophe were leads to significant efforts in early detection through medical imaging. This paper presents a Deep Learning (DL)-based method for automatically identifying Covid-19 from X-ray images of chest using a robust pipeline. The proposed method begins with denoising input images using Efficient Channel and Spatial Attention (ECSA) scheme to improve image quality and drop irrelevant noise. Feature extraction is done using Deep Convolutional Neural Network (DCNN) augmented with Multi-Head Channel Attention (MHCA) which facilitates the model to focus on multiple relevant feature representations simultaneously. For classification, a Modified CNN (MCNN) framework which is optimized for distinguishing amid Covid-19, Normal and Pneumonia cases is employed. Performance of the propounded model is assessed on Kaggle dataset and Curated dataset compiled from multiple public sources. Results demonstrate effectiveness of model with performance assessed based on Accuracy, Recall, Precision and F-measure, showing promising diagnostic capabilities across diverse datasets.