Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images

Abstract

The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million people globally. For its early diagnosis, researchers consider chest X-ray examinations as a standard screening technique in addition to RT-PCR test. Majority of research work till date focused only on application of deep learning approaches that is relevant but lacking in better pre-processing of CXR images. Towards this direction, this study aims to explore cumulative effects of image denoising and enhancement approaches on the performance of deep learning approaches. Regarding pre-processing, suitable methods for X-ray images, Histogram equalization, CLAHE and gamma correction have been tested individually and along with adaptive median filter, median filter, total variation filter and gaussian denoising filters. Proposed study compared eleven combinations in exploration of most coherent approach in greedy manner. For more robust analysis, we compared ten CNN architectures for performance evaluation with and without enhancement approaches. These models are InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, Vgg19, NASNetMobile, ResNet101, DenseNet121, DenseNet169, DenseNet201. These models are trained in 4-way (COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal) and 3-way classification scenario (COVID-19 vs Pneumonia vs Normal) on two benchmark datasets. The proposed methodology determines with TVF + Gamma, models achieve higher classification accuracy and sensitivity. In 4-way classification MobileNet with TVF + Gamma achieves top accuracy of 93.25% with 1.91% improvement in accuracy score, COVID-19 sensitivity of 98.72% and F1-score of 92.14%. In 3-way classification our DenseNet201 with TVF + Gamma gains accuracy of 91.10% with improvement of 1.47%, COVID-19 sensitivity of 100% and F1-score of 91.09%. Proposed study concludes that deep learning modes with gamma correction and TVF + Gamma has superior performance compared to state-of-the-art models. This not only minimizes overlapping between COVID-19 and virus pneumonia but advantageous in time required to converge best possible results.

Keywords: COVID-19 analysis; Chest X-ray; Deep learning; Image denoising; Image enhancement; Pneumonia classification.

Fig. 1

Block diagram of two ways and sequence of basic elements for analysis of COVID-19 among other pathology classes

Fig. 2

Framework diagrams for image enhancement approaches; where (a) corresponds to Histogram equalization, (b) represents CLAHE flowchart, (c) represents Gamma correction flowchart

Fig. 3

Sequence of image enhancement and denoising combinations tested for finding best enhancement technique

Fig. 4

Proposed image classification pipeline

Fig. 5

Visualization of effects on image and its pixel distribution after various preprocessing steps

Fig. 6

Contribution of each class to loss function with and without weighted factor

Fig. 7

Classification network architecture

Fig. 8

Visualization of validation accuracy and validation loss vs epochs under various preprocessing combinations by DenseNet201 model

Fig. 9

Models’ comparison of accuracy and COVID-19 sensitivity with and without TVF + Gamma corresponding to COVIDx dataset

Fig. 10

Models’ comparison of accuracy and COVID-19 sensitivity with and without TVF + Gamma corresponding to COVIDz dataset

Fig. 11

Accuracy comparison of models with various image enhancement combinations in experiment 1(COVIDx) and experiment 2 (COVIDz)

Fig. 12

Visualization of validation accuracy vs epochs of CNN models with preprocessing pipeline as CLAHE, gamma correction and TVF + Gamma in 4-way classification using COVIDx dataset

Fig. 13

Confusion matrices by DenseNet201 and MobileNet model corresponding to 4-class configurations with various enhancement techniques. Subplots a, b, c, d, e corresponds to DenseNet201 model for comparison of image enhancement combinations and subplot f), corresponds to best performing MobileNet model with TVF + Gamma

Fig. 14

Class-wise sensitivity analysis of various models with respect to image enhancement techniques on COVIDx dataset

Fig. 15

Comparison of various image enhancement techniques at different threshold values by considering DenseNet201 model with COVIDx dataset; where (a) corresponds to accuracy comparison, (b) represents Sensitivity comparison and (c) represents COVID-19 sensitivity comparison

Fig. 16

Effects of different image enhancement techniques on attention visualization by Grad-CAM of correctly classified COVID-19 CXR images using different models