DEEP FEATURE WISE ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR COVID-19 DETECTION USING LUNG CT SCAN IMAGES

Abstract

This study anticipates to identify Covid-19 using Lung CT (Computed Tomography) scan image dataset with the help of effective DL(Deep Learning) based algorithms. Though several clinical procedures and imaging modalities exists to diagnose Covid-19, these methods are time-consuming processes and sometimes the predictions are incorrect. Concurrently, AI (Artificial Intelligence) based DL models have gained attention in this area due to its innate capability for efficient learning. Though conventional systems have tried to perform better prediction, they lacked in accuracy with prediction rate. Moreover, the conventional systems have not utilized attention model completely for Covid-19 detection. This research is intended to resolve these pitfalls of covid-19 detection methods with the help of deep feature wise attention based Convolutional Neural Network. For this purpose, the data has been pre-processed by image resizing, the Residual Descriptor with Conv-BAM(Convolutional Block Attention Module) has been employed to obtain refined features from spatial and channel wise attention based module. The obtained features are used in the present study to improvise the classification as covid positive or negative. The performance of the proposed system has been assessed with regard to metrics to prove better efficiency. The proposed method achieved high accuracy rate of 97.82%. This DL based model can be used as a supplementary tool in the diagnosis of Covid-19 alongside other diagnostic method.