Ratul, Rizwanul HoqueHusain, Farah AnjumPurnata, Tajmim HossainPomil, Rifat AlamKhandoker, ShaimaParvez, Mohammad Zavid2026-08-172026-08-172021-01-01R. H. Ratul, F. A. Husain, T. H. Purnata, R. A. Pomil, S. Khandoker and M. Z. Parvez, "Multi-Stage Optimization of Deep Learning Model to Detect Thoracic Complications," 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, 2021, pp. 3000-3005, doi: 10.1109/SMC52423.2021.9658904.1062922X2-s2.0-85124276994https://hdl.handle.net/10361/29166The diagnosis of thoracic diseases, many of which are easily treatable, is mainly done with the use of chest X-rays and other chest imaging techniques and making sense of these require expert radiologists, who aren't always accessible. Using Deep Neural Networks, patterns corresponding to different thoracic diseases can be detected from these chest X-rays with the aid of machines. In this study, we have proposed such a model which can detect the presence of 14 different thoracic diseases from a chest X-ray alone. Our novel dense convolutional neural network model works in 2 stages, first training on images from disease-ridden patients alone, and then training the entire network on the whole dataset which includes X-rays from both healthy and unhealthy patients. Given a chest X-ray alone, our model can give accurate predictions, with an AUROC mean score of 82.9% competing with the existing state-of-the-art models in this field.3000-3005en-USfalseChest X-rayConvolutional neural networksDeep learningDenseNetThoracic diseasesChest--Radiography.Machine learning.Conference Proceeding10.1109/SMC52423.2021.9658904