Multi-Label Classification of Lung Diseases Using Deep Learning - Muhammad Irtaza; Arshad Ali; Maryam Gulzar; Aamir Wali (IEEE Access, 2024)
Assistance for doctors in disease detection can be very useful in environments with scarce resources and personnel. Historically, many patients could have been cured with early detection of the disease. The application of deep learning techniques in the fields of medical imaging, on large datasets, has allowed computer algorithms to produce as effective results as medical professionals. To assist doctors, it is essential to have a versatile system that can timely detect multiple diseases in the lungs with high accuracy. Over time, although many classifiers and algorithms have been implemented, however, deep learning models (i.e., CNN, Deep-CNN, and R-CNN) are known to offer better results. After a thorough literature review of the state-of-the-art techniques, this work applies various models such as MobileNet, DenseNet, VGG-16, EfficientNet, Xception, and InceptionV3 to the selected large dataset. The goal is to enhance the accuracy of these algorithms by experimenting with parameter optimizations. We observe that MobileNet produces better results as compared to other models. We implement a deep convolutional GAN to produce synthetic X-ray images containing various pathologies already included in the chosen imbalanced dataset namely NIH Chest X-ray containing 14 classes. The synthetic dataset contains 1193 samples belonging to five classes. We test the suggested model using evaluation measures like recall, precision, and F1-score, along with binary accuracy. The suggested deep learning model produces recall as high as 57%, binary accuracy as 93.4%, F1-Score as 0.553, and AUC as 81. After the inclusion of generated synthetic samples, the value of the F1-score becomes 0.582 resulting in a 5% increase. Though, Generative Adversarial Network (GAN) shows lower performance, however, we encourage further research and experiments to find the versatility of GANs in the field of medical imaging.