A Data Augmented Approach to Transfer Learning for Covid-19 Detection

TL;DR

This paper demonstrates that augmenting limited Covid-19 X-ray datasets with CLAHE improves transfer learning performance across various deep models, notably enhancing sensitivity and mitigating sample bias.

Contribution

The study introduces a CLAHE-based data augmentation method to enhance transfer learning for Covid-19 detection from limited X-ray datasets, improving model reliability.

Findings

CLAHE augmentation significantly boosts model sensitivity.

VGG-16 with CLAHE achieves 95% sensitivity in 15 epochs.

Augmentation reduces sample bias impact on transfer learning.

Abstract

Covid-19 detection at an early stage can aid in an effective treatment and isolation plan to prevent its spread. Recently, transfer learning has been used for Covid-19 detection using X-ray, ultrasound, and CT scans. One of the major limitations inherent to these proposed methods is limited labeled dataset size that affects the reliability of Covid-19 diagnosis and disease progression. In this work, we demonstrate that how we can augment limited X-ray images data by using Contrast limited adaptive histogram equalization (CLAHE) to train the last layer of the pre-trained deep learning models to mitigate the bias of transfer learning for Covid-19 detection. We transfer learned various pre-trained deep learning models including AlexNet, ZFNet, VGG-16, ResNet-18, and GoogLeNet, and fine-tune the last layer by using CLAHE-augmented dataset. The experiment results reveal that the CLAHE-based augmentation to various pre-trained deep learning models significantly improves the model efficiency. The pre-trained VCG-16 model with CLAHEbased augmented images achieves a sensitivity of 95% using 15 epochs. AlexNet works show good sensitivity when trained on non-augmented data. Other models demonstrate a value of less than 60% when trained on non-augmented data. Our results reveal that the sample bias can negatively impact the performance of transfer learning which is significantly improved by using CLAHE-based augmentation.