Chest Radiographs Classification Using Multi-model Deep Learning: A Comparative Study

Abstract

Respiratory diseases have been a main reason for death in many countries worldwide. This study considers Pneumonia which is a common lung infection condition and COVID-19 which was declared a pandemic in 2020. Since both diseases can lead to life-threatening conditions, detecting these conditions at an early stage is crucial to properly treat the patients. While chest X-rays are widely used for diagnosing these diseases, it requires expert knowledge. This study focuses on introducing a deep learning based approach for analysing chest X-ray images to detect normal, Pneumonia and COVID-19 conditions. Experiments were conducted with multi-model deep learning models including MobileNetV2, Resnet50, InceptionV3, and Xception architectures with added layers, and 5-fold cross-validation. The results of ResNet50 show an average accuracy and recall of 98.87% and 98.54%, respectively.

Ähnliche Arbeiten