A Lightweight Hybrid DL Model for Multi-Class Chest X-ray Classification for Pulmonary Diseases.
Authors
Affiliations (4)
Affiliations (4)
- Biomedical Engineering, Rajalakshmi Engineering College, Centre of Excellence in Medical Imaging, Chennai, Tamilnadu, 602105, INDIA.
- \, Rajalakshmi Engineering College, Centre of Excellence in Medical Imaging, Department of Biomedical Engineering, \, Chennai, Tamilnadu, 602105, INDIA.
- Biomedical Engineering, Rajalakshmi Engineering College, Centre of Excellence in Medical Imaging, Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamilnadu, 602105, INDIA.
- Capgemini India, Department of Medical Devices and Healthcare Technologies,, Bangalore, MH, 560066, INDIA.
Abstract
Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and efficiency. Timely and precise diagnosis of these diseases proves to be invaluable for effective treatment procedures. Chest X-rays (CXR) perform a pivotal role in diagnosing various respiratory diseases by offering valuable insights into the chest and lung regions. This study puts forth a hybrid approach for classifying CXR images into four classes namely COVID-19, tuberculosis, pneumonia, and normal (healthy) cases. The presented method integrates a machine learning method, Support Vector Machine (SVM), with a pre-trained deep learning model for improved classification accuracy and reduced training time. Data from a number of public sources was used in this study, which represents a wide range of demographics. Class weights were implemented during training to balance the contribution of each class in order to address the class imbalance. Several pre-trained architectures, namely DenseNet, MobileNet, EfficientNetB0, and EfficientNetB3, have been investigated, and their performance was evaluated. Since MobileNet achieved the best classification accuracy of 94%, it was opted for the hybrid model, which combines MobileNet with SVM classifier, increasing the accuracy to 97%. The results suggest that this approach is reliable and holds great promise for clinical applications.
.