Deep learning (DL) has revolutionized the field of medical imaging, including chest radiology, by offering advanced tools for accurate and efficient detection of diseases for over a decade now. Analysis of Chest radiology images (chest X-ray - CXR and Computed tomography - CT) using DL models has widened its scope as a triaging tool since COVID-19 pandemic due to its speed, accuracy, and objectivity of disease detection, leading to better patient outcomes and more efficient healthcare delivery. CNNs are particularly well suited for image analysis tasks due to their ability to capture hierarchical features. Earlier work on Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) have also shown their applicability in the diagnosis of pulmonary diseases. This has led to much recent attention on the analysis of chest radiographs (CXR) using deep learning architectures for the detection of COVID-19 in a clinical setting. Applications developed for medical image analysis require high sensitivity, precision and generalizability along with reliability so as to provide radiologists and clinicians with an additional layer of information to aid in diagnoses. In this work we propose pixel-based attention mechanisms into a lightweight CNN model (Attn-CNN) trained on one of the largest publicly available COVIDx CXR-3 dataset. With much fewer training parameters, it is seen to perform better than four state-of-the-art (SOTA) deep learning models. The generalizability of the model is shown by performing analysis on external dataset. With portable chest radiography (CXR) being commonly used for early disease detection and follow up of lung abnormalities, there is a clear scope of the proposed model in assisting health experts in triaging of patients in pandemic-like situations. Data and code are available at: https://github.com/aleesuss/c19 .