NSU Research Contributions
Title : Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization
Authors : MUHAMMAD E. H. CHOWDHURY TAWSIFUR RAHMAN, AMITH KHANDAKAR, MUHAMMAD ABDUL KADIR, KHANDAKER REJAUL ISLAM, KHANDAKAR F. ISLAM, RASHID MAZHAR, TAHIR HAMID, MOHAMMAD TARIQUL ISLAM, SAAD KASHEM, ZAID BIN MAHBUB, MOHAMED ARSELENE AYARI
Abstract : Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image preprocessing, data augmentation, image segmentation, and deep-learning classication techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classication using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specicity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classication using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specicity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to conrm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.
| Journal : IEEE Access | Volume : 8 | Year : 2020 | Issue : |
| Pages : 191586-191601 | City : | Edition : | Editors : |
| Publisher : | ISBN : | Book : | Chapter : |
| Proceeding Title : | Institution : | Issuer : | Number : |
