EXPLORING THE IMPACT OF IMAGE ENHANCEMENT AND DATA AUGMENTATION TECHNIQUES ON LUNG DETECTION IN CHEST RADIOGRAPHY IMAGES

The health and wellbeing of people all over the world are being severely impacted by the ongoing COVID-19 pandemic. One of the most important ways to check for COVID-19 is chest radiography, so ensuring that infected people undergo this test is crucial. This research set out to assess the efficacy of various image enhancement and data augmentation techniques for use with digital chest X-Rays in the detection of COVID-19 patients. White-balance correction (WB) and contrast-limited adaptive histogram equalization (CLAHE) were the two methods used to improve the images. These two technologies have also been applied to examine this impact on COVID-19 discrimination. Also, Data was augmented in two distinct ways, using a different set of techniques and combining it with image enhancement techniques. Transfer learning was used to compare image classification models pre-trained on the ImageNet dataset to well-known deep learning architectures. Our models were evaluated and compared using the novel-combined chest X-Ray datasets. We observed that the VGG-16 model outperforms other models with an accuracy of 98% when image WB and CLAHE are used together. Due to their superior performance, these pre-trained models can greatly improve the speed and accuracy of COVID-19 diagnosis. © 2023, International Organization on 'Technical and Physical Problems of Engineering'. All rights reserved.

Highly accurate multiclass classification of respiratory system diseases from chest radiography images using deep transfer learning technique

Chest X-ray radiographic (CXR) imaging aids in the early and accurate diagnosis of lung disease. The diagnosis process can be automated and accelerated by analyzing chest CXR images with artificial intelligence tools, particularly Convolutional Neural Network (CNN). Due to few medical images have been labeled, the most significant obstacle is utilizing these images accurately for diagnosing and tracking disease progression, and accordingly, the difficulty of automating the classification of these images into positive and negative cases. To address this issue, a deep CNN model was proposed to classify respiratory system diseases from X-ray images using a transfer learning technique based on the EfficientNetV2 model that acts as a backbone to enhance the efficacy and accuracy of Computer-Assisted Diagnosis (CAD) performance. Moreover, the latest data augmentation methods and fine-tuning for the last block in the convolutional base have also been carried out. In addition, Grad-CAM is used to highlight the important features and make the deep learning model more comprehensible. The proposed model is trained to work on the triple classification, COVID-19, normal, and pneumonia. It uses CXR images from three publicly accessible datasets. The following performance was achieved on the testing set: sensitivity = 98.66 %, specificity = 99.51 %, and accuracy = 99.4 %. Thereby, the proposal outperforms the four most recent classification techniques in the literature.Copyright © 2023 Elsevier Ltd