HANDLING SEVERE DATA IMBALANCE IN CHEST X-RAY IMAGE CLASSIFICATION WITH TRANSFER LEARNING USING SWAV SELF-SUPERVISED PRE-TRAINING

Ever since the COVID-19 outbreak, numerous researchers have attempted to train accurate Deep Learning (DL) models, especially Convolutional Neural Networks (CNN), to assist medical personnel in diagnosing COVID-19 infections from Chest X-Ray (CXR) images. However, data imbalance and small dataset sizes have been an issue in training DL models for medical image classification tasks. On the other hand, most researchers focused on complex novel methods instead and few explored this problem. In this research, we demonstrated how Self-Supervised Learning (SSL) can assist DL models during pre-training, and Transfer Learning (TL) can be used in training the models, which can produce models that are more robust to data imbalance. The Swapping Assignment between Views (SwAV) algorithm in particular has been known to be outstanding in enhancing the accuracy of CNN models for classification tasks after TL. By training a ResNet-50 model pre-trained using SwAV on a severely imbalanced CXR dataset, the model managed to greatly outperform its counterpart pre-trained in a standard supervised manner. The SwAV-TL ResNet-50 model attained 0.952 AUROC with 0.821 macro-averaged F1 score when trained on the imbalanced dataset. Hence, it was proven that TL using models pre-trained through SwAV can achieve better accuracy even when the dataset is severely imbalanced, which is usually the case in medical image datasets. © 2023, SCIK Publishing Corporation. All rights reserved.

IMPROVING LUNG DISEASE DETECTION BY JOINT LEARNING WITH COVID-19 RADIOGRAPHY DATABASE

Diagnostic chest radiography is one of the most common imaging tests performed in medical practice. A radiology workflow goal is to detect, diagnose, and manage diseases using chest radiography in an automated, timely, and accurate manner. Radiography data have proved very effective for assessing COVID-19 patients, particularly for treating overcrowded emergency departments and hospitals. The use of Deep Learning (DL) methods in Artificial Intelligence (AI) has become dominant in detecting diseases via chest X-rays. This study utilized the COVID-19 Radiographic Database and the National Institutes of Health (NIH) Chest-Xray to study pre-training fine-tuning of the DL model on chest radiographic images. We investigate the robust network architecture in detail: DenseNet-121, in this dataset dual technique to improve insight into the different methods and their application to chest X-ray classification. Consequently, this dual dataset technique is able to provide better detection results for each cluster of lung diseases. AUC results obtained using DenseNet-121 reached an average of 82.16 percent, with the highest AUC reaching 99.99% in the cluster containing Viral Pneumonia lung disease. © 2022 the author(s).