COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19

TL;DR

COVIDLite is a lightweight deep neural network combining image preprocessing and depth-wise separable convolutions, achieving high accuracy in COVID-19 detection from chest X-rays, suitable for mobile integration and rapid diagnosis.

Contribution

This paper introduces COVIDLite, a novel deep learning model with preprocessing techniques that enhances COVID-19 detection accuracy while maintaining a lightweight architecture for mobile use.

Findings

Achieved 99.58% accuracy in binary classification

Outperformed state-of-the-art methods in COVID-19 detection

Model size is only 8.4 MB, suitable for mobile deployment

Abstract

Background and Objective:Currently, the whole world is facing a pandemic disease, novel Coronavirus also known as COVID-19, which spread in more than 200 countries with around 3.3 million active cases and 4.4 lakh deaths approximately. Due to rapid increase in number of cases and limited supply of testing kits, availability of alternative diagnostic method is necessary for containing the spread of COVID-19 cases at an early stage and reducing the death count. For making available an alternative diagnostic method, we proposed a deep neural network based diagnostic method which can be easily integrated with mobile devices for detection of COVID-19 and viral pneumonia using Chest X-rays (CXR) images. Methods:In this study, we have proposed a method named COVIDLite, which is a combination of white balance followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and depth-wise separable convolutional neural network (DSCNN). In this method, white balance followed by CLAHE is used as an image preprocessing step for enhancing the visibility of CXR images and DSCNN trained using sparse cross entropy is used for image classification with lesser parameters and significantly lighter in size, i.e., 8.4 MB without quantization. Results:The proposed COVIDLite method resulted in improved performance in comparison to vanilla DSCNN with no pre-processing. The proposed method achieved higher accuracy of 99.58% for binary classification, whereas 96.43% for multiclass classification and out-performed various state-of-the-art methods. Conclusion:Our proposed method, COVIDLite achieved exceptional results on various performance metrics. With detailed model interpretations, COVIDLite can assist radiologists in detecting COVID-19 patients from CXR images and can reduce the diagnosis time significantly.