Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
By Tawsifur Rahman, Muhammad Enamul Hoque Chowdhury, Amith Abdullah Khandakar, Khandaker Reajul Islam, Khandaker F. Islam, Zaid Bin Mahbub, Muhammad Abdul Kadir, S. Kashem. Applied Sciences, 2020
565 citations recorded by the source corpus.
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/1028dfba19e38aa0f41eb3ba2b4885e835d63642
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Pulmonary and Respiratory Medicine
- Mohammad Tariqul Islam
- Muhammad Abdul Kadir
- Tawsifur Rahman
- Khandaker F. Islam
- Khandaker Reajul Islam
- Amith Abdullah Khandakar
- Muhammad Enamul Hoque Chowdhury
- Zaid Bin Mahbub
In Radiology, Nuclear Medicine and Imaging
- Mohammad Tariqul Islam
- Muhammad Abdul Kadir
- Tawsifur Rahman
- Khandaker F. Islam
- Khandaker Reajul Islam
- Amith Abdullah Khandakar
- Muhammad Enamul Hoque Chowdhury
- Zaid Bin Mahbub
Citations in the corpus
- Convolutional Neural Networks: A Survey (2023, unknown): “Transfer learning is a technique that allows CNNs to leverage the knowledge learned from one task to improve performance on a second, related task [188–192].”
- Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques (2021, methodology): “During the COVID-19 pandemic, AI was used extensively for the identification of lung-related diseases [8–11] as well as other prognostic applications [12].”
- Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization (2020, background): “breast cancer detection, physiological monitoring and social sensing [9-15] .”
- Can AI Help in Screening Viral and COVID-19 Pneumonia? (2020, methodology): “) are using Artificial Intelligence (AI) based solutions [20]–[25].”