A CNN‐Based Chest Infection Diagnostic Model: A Multistage Multiclass Isolated and Developed Transfer Learning Framework
Muhammad Umair Ali, Karam Dad Kallu, Haris Masood, Usama Tahir, Chandu V.V. Muralee Gopi, Amad Zafar, Seung Won Lee
Abstract
In 2019, a deadly coronaviral infection (COVID‐19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID‐19 may be found using a chest X‐ray scan, a reliable imaging method. Although an expert may examine an X‐ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19‐layer CNN model is developed from scratch to detect chest infections using X‐rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID‐19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four‐class subclassification (COVID‐19, fibrosis, pneumonia, and tuberculosis) for an online chest X‐ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.