Leveraging Attention Mechanisms to Enhance EfficientNet for Precise Analysis of Chest CT Images

Accurate and timely analysis of chest CT images is crucial for effectively diagnosing and treating a wide range of respiratory, cardiovascular, and infectious diseases, making it a vital component of modern medicine. Manual interpretation of chest CT can be time-consuming, prone to subjective variability, and potentially error-prone, highlighting the need for automation to improve efficiency and accuracy in analyzing large volumes of images. Researchers are increasingly using convolutional neural networks (CNNs) for chest CT image analysis due to their ability to learn complex features and patterns from large datasets. Despite significant advancements in analytical techniques, the challenge of determining which regions of interest to focus on and how to assign appropriate levels of importance to various features during chest CT image analysis remains an ongoing concern in the field. Our paper introduces a novel approach, utilizing soft and channel attention mechanisms in conjunction with an improved version of EfficientNetB0 to efficiently extract and prioritize critical features essential for accurately detecting and diagnosing lung-related ailments from chest CT scans. We have evaluated the proposed approach on a large dataset of CT scans, with the objective of accurately identifying COVID-19, non-COVID-19, and Community-Acquired Pneumonia (CAP) cases. Experimental results demonstrated exceptional performance, with achieved accuracy of 99.41%, surpassing other state-of-the-art methods in the field.