Comparative Study of Transfer Learning techniques for Lung Disease prediction

ABSTRACT

Lung Disease is one of the most common healthcare issues in the entire world in all age groups of humans due to the cause of smoking, infection and contaminated air. Pneumonia, Coronavirus disease 2019 (COVID-19) and Tuberculosis are the common types of lung disease. To reduce the diagnosis of the Lung Disease, several research works have been done with the help of Artificial Intelligence based-technologies. Prediction and classification of Lung Disease are the common tasks using Chest X-ray images, CT scan images, and MRI images with the help of machine learning, deep learning, and transfer learning models. The main idea of this paper is to show the comparative analysis of ResNet-50, MobileNet-V2, VGG-19 and DenseNet201 CNN-based Transfer Learning techniques and suggesting which image classification algorithm is more recommended to predicting and classifying Lung Disease classification problem. Those four algorithms have been trained on the X-ray images and evaluated using performance metrics techniques. The results have been shown 94.64% accuracy obtained using ResNet50, 97.43% accuracy obtained using VGG19, 98.49% accuracy obtained using MobileNetV2, and 98.05% accuracy obtained using DenseNet201 pre-trained model. Based on the experimental results MobileNet model has been outperformed as compared to other models. Training time per epoch and ROC-AUC values of MobileNet was better than other models. Based on our studies, MobileNet transfer learning is recommended pre-trained model for Lung Disease prediction and classification problem. © 2021 IEEE.