ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep Learning

Abstract

Lung abnormalities are among the significant contributors to morbidity and mortality worldwide. It induces symptoms like coughing, sneezing, fever, breathlessness, etc., which, if left untreated, may lead to death. In current clinical practice, chest X-ray (CXR) images are widely preferred to diagnose different lung abnormalities. However, the pathological tests are time-consuming, expensive and require domain experts. On the other hand, diagnosis through CXR images is manual and subject to inter-observer and intra-observer variability. The recent advancement in deep learning (DL) algorithms may be employed to address these challenges. However, the selection of correct algorithms along with finetuned parameters is challenging. In this study, we comprehensively compared the performance of two state-of-the-art DL algorithms, Resnet-50 and Efficient-B0. These two models are pervasively used in literature and have shown promising classification performance. The performance of the used algorithms is validated using the multi-centric dataset from Kaggle (having Covid-19, Normal, Pneumonia classes) and Mendeley chest X-ray dataset (having Normal, Pneumonia-Bacterial, Pneumonia-Viral, Covid-19 classes). Upon training the algorithms with a mini-batch size 32 and a maximum epoch of 40 using training data, we achieved 0.9807 and 0.9874 accuracy for Kaggle Dataset and Mendeley Dataset, respectively. Similarly, we achieved 0.9962 and 9.9978 for Kaggle dataset and Mendeley dataset for test data, respectively. From the result, it is evident that the EfficientNet-B0 model outperformed the multi-centric datasets.