{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T15:54:19Z","timestamp":1787846059038,"version":"build-2784847793"},"reference-count":65,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,23]],"date-time":"2020-03-23T00:00:00Z","timestamp":1584921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1D1A1B07041921"],"award-info":[{"award-number":["NRF-2018R1D1A1B07041921"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1A2C1083813"],"award-info":[{"award-number":["NRF-2019R1A2C1083813"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1F1A1041123"],"award-info":[{"award-number":["NRF-2019R1F1A1041123"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.<\/jats:p>","DOI":"10.3390\/jcm9030871","type":"journal-article","created":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T13:04:04Z","timestamp":1585055044000},"page":"871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-5207","authenticated-orcid":false,"given":"Muhammad","family":"Arsalan","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7679-081X","authenticated-orcid":false,"given":"Muhammad","family":"Owais","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-9532","authenticated-orcid":false,"given":"Tahir","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiho","family":"Choi","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","article-title":"Fully convolutional architectures for multiclass segmentation in chest radiographs","volume":"37","author":"Novikov","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137794","DOI":"10.1109\/ACCESS.2019.2941511","article-title":"Segmentation of lung in chest radiographs using hull and closed polygonal line method","volume":"7","author":"Peng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s11548-019-01917-1","article-title":"A review on lung boundary detection in chest X-rays","volume":"14","author":"Candemir","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.medengphy.2012.03.011","article-title":"A computer-aided diagnosis approach for emphysema recognition in chest radiography","volume":"35","author":"Coppini","year":"2013","journal-title":"Med. Eng. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ejrad.2010.08.021","article-title":"Computer-aided recognition of emphysema on digital chest radiography","volume":"80","author":"Miniati","year":"2011","journal-title":"Eur. J. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.medengphy.2006.02.001","article-title":"Computer-aided diagnosis of emphysema in COPD patients: Neural-network-based analysis of lung shape in digital chest radiographs","volume":"29","author":"Coppini","year":"2007","journal-title":"Med. Eng. Phys."},{"key":"ref_7","first-page":"187","article-title":"Cardiomegaly is a common arrhythmogenic substrate in adult sudden cardiac deaths, and is associated with obesity","volume":"44","author":"Tavora","year":"2012","journal-title":"Pathology (Phila.)"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Candemir, S., Jaeger, S., Lin, W., Xue, Z., Antani, S., and Thoma, G. (March, January 28). Automatic heart localization and radiographic index computation in chest x-rays. Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, CA, USA.","DOI":"10.1117\/12.2217209"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.cmpb.2011.07.009","article-title":"Automatic evaluation of cardiac hypertrophy using cardiothoracic area ratio in chest radiograph images","volume":"105","author":"Hasan","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s10278-003-1900-3","article-title":"Extraction of the two-dimensional cardiothoracic ratio from digital PA chest radiographs: Correlation with cardiac function and the traditional cardiothoracic ratio","volume":"17","author":"Browne","year":"2004","journal-title":"J. Digit. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W., and Xing, E.P. (2018). Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio. arXiv.","DOI":"10.1007\/978-3-030-00934-2_61"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Solovyev, R., Melekhov, I., Lesonen, T., Vaattovaara, E., Tervonen, O., and Tiulpin, A. (2020, January 10\u201314). Bayesian feature pyramid networks for automatic multi-label segmentation of chest X-rays and assessment of cardio-thoratic ratio. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Auckland, New Zealand.","DOI":"10.1007\/978-3-030-40605-9_11"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Brakohiapa, E.K.K., Botwe, B.O., Sarkodie, B.D., Ofori, E.K., and Coleman, J. (2017). Radiographic determination of cardiomegaly using cardiothoracic ratio and transverse cardiac diameter: Can one size fit all? Part one. Pan Afr. Med. J., 27.","DOI":"10.11604\/pamj.2017.27.201.12017"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Owais, M., Arsalan, M., Choi, J., Mahmood, T., and Park, K.R. (2019). Artificial intelligence-based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis. J. Clin. Med., 8.","DOI":"10.3390\/jcm8070986"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Cho, S.W., and Park, K.R. (2019). Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation. J. Clin. Med., 8.","DOI":"10.3390\/jcm8091446"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Owais, M., Arsalan, M., Choi, J., and Park, K.R. (2019). Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. J. Clin. Med., 8.","DOI":"10.3390\/jcm8040462"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Pham, T.D., Batchuluun, G., Yoon, H.S., and Park, K.R. (2019). Artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains. J. Clin. Med., 8.","DOI":"10.3390\/jcm8111976"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.celrep.2019.05.109","article-title":"Ultra-sensitive TP53 sequencing for cancer detection reveals progressive clonal selection in normal tissue over a century of human lifespan","volume":"28","author":"Salk","year":"2019","journal-title":"Cell Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hwang, E.J., Nam, J.G., Lim, W.H., Park, S.J., Jeong, Y.S., Kang, J.H., Hong, E.K., Kim, T.M., Goo, J.M., and Park, S. (2019). Deep learning for chest radiograph diagnosis in the emergency department. Radiology.","DOI":"10.1148\/radiol.2019191225"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TIP.2019.2919937","article-title":"High-resolution encoder\u2013decoder networks for low-contrast medical image segmentation","volume":"29","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110674","DOI":"10.1109\/ACCESS.2019.2934486","article-title":"An accurate nuclei segmentation algorithm in pathological image based on deep semantic network","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gordienko, Y., Gang, P., Hui, J., Zeng, W., Kochura, Y., Alienin, O., Rokovyi, O., and Stirenko, S. (2019, January 18\u201320). Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. Proceedings of the Advances in Computer Science for Engineering and Education, Kiev, Ukraine.","DOI":"10.1007\/978-3-319-91008-6_63"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s11277-018-5702-9","article-title":"LF-SegNet: A fully convolutional encoder\u2013decoder network for segmenting lung fields from chest radiographs","volume":"101","author":"Mittal","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","article-title":"Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration","volume":"33","author":"Candemir","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","unstructured":"Jaeger, S., Karargyris, A., Antani, S., and Thoma, G. (September, January 28). Detecting tuberculosis in radiographs using combined lung masks. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Supanta, C., Kemper, G., and del Carpio, C. (2018, January 17\u201319). An algorithm for feature extraction and detection of pulmonary nodules in digital radiographic images. Proceedings of the IEEE International Conference on Automation\/XXIII Congress of the Chilean Association of Automatic Control, Concepcion, Chile.","DOI":"10.1109\/ICA-ACCA.2018.8609795"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jangam, E., and Rao, A.C.S. (2019, January 21\u201322). Segmentation of lungs from chest X rays using firefly optimized fuzzy C-means and level set algorithm. Proceedings of the Recent Trends in Image Processing and Pattern Recognition, Solapur, India.","DOI":"10.1007\/978-981-13-9184-2_27"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vital, D.A., Sais, B.T., and Moraes, M.C. (2018, January 21\u201325). Automatic pulmonary segmentation in chest radiography, using wavelet, morphology and active contours. Proceedings of the XXVI Brazilian Congress on Biomedical Engineering, Arma\u00e7\u00e3o de Buzios, RJ, Brazil.","DOI":"10.1007\/978-981-13-2517-5_12"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wan Ahmad, W.S.H.M., Zaki, W.M.D., and Ahmad Fauzi, M.F. (2015). Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter. Biomed. Eng. OnLine, 14.","DOI":"10.1186\/s12938-015-0014-8"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Iakovidis, D.K., and Papamichalis, G. (2008, January 10\u201312). Automatic segmentation of the lung fields in portable chest radiographs based on B\u00e9zier interpolation of salient control points. Proceedings of the IEEE International Workshop on Imaging Systems and Techniques, Crete, Greece.","DOI":"10.1109\/IST.2008.4659946"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pattrapisetwong, P., and Chiracharit, W. (2016, January 14\u201317). Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding. Proceedings of the International Computer Science and Engineering Conference, Chiang Mai, Thailand.","DOI":"10.1109\/ICSEC.2016.7859887"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, L., and Chen, J. (2017, January 15\u201317). A visual saliency-based method for automatic lung regions extraction in chest radiographs. Proceedings of the 14th International Computer Conference on Wavelet Active Media Technology and Information Processing, Chengdu, China.","DOI":"10.1109\/ICCWAMTIP.2017.8301470"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"135192","DOI":"10.1109\/ACCESS.2019.2941923","article-title":"Smart pleural effusion drainage monitoring system establishment for rapid effusion volume estimation and safety confirmation","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1049\/iet-cvi.2009.0141","article-title":"Lung segmentation in chest radiographs by fusing shape information in iterative thresholding","volume":"5","author":"Dawoud","year":"2011","journal-title":"IET Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Saad, M.N., Muda, Z., Ashaari, N.S., and Hamid, H.A. (2014, January 28\u201330). Image segmentation for lung region in chest X-ray images using edge detection and morphology. Proceedings of the IEEE International Conference on Control System, Computing and Engineering, Batu Ferringhi, Malaysia.","DOI":"10.1109\/ICCSCE.2014.7072687"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s11517-015-1412-6","article-title":"Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models","volume":"54","author":"Lee","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.neucom.2017.09.053","article-title":"Low order adaptive region growing for lung segmentation on plain chest radiographs","volume":"275","author":"Chondro","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2018.2837901","article-title":"Automatic lung segmentation with juxta-pleural nodule identification using active contour model and bayesian approach","volume":"6","author":"Chung","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., and Xing, E.P. (2018, January 20). SCAN: Structure correcting adversarial network for organ segmentation in chest X-rays. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_30"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W., and Xing, E. (2018, January 16\u201320). Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio. Proceedings of the Medical Image Computing and Computer Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_61"},{"key":"ref_42","unstructured":"Tang, Y.-B., Tang, Y.-X., Xiao, J., and Summers, R.M. (2019, January 8\u201310). XLSor: A robust and accurate lung segmentor on chest X-rays using criss-cross attention and customized radiorealistic abnormalities generation. Proceedings of the International Conference on Medical Imaging with Deep Learning, London, UK."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.cmpb.2019.06.005","article-title":"An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks","volume":"177","author":"Souza","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Venkataramani, R., Ravishankar, H., and Anamandra, S. (2019, January 8\u201311). Towards continuous domain adaptation for medical imaging. Proceedings of the IEEE 16th International Symposium on Biomedical Imaging, Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759268"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Oliveira, H., and dos Santos, J. (November, January 29). Deep transfer learning for segmentation of anatomical structures in chest radiographs. Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images, Parana, Brazil.","DOI":"10.1109\/SIBGRAPI.2018.00033"},{"key":"ref_46","unstructured":"Islam, J., and Zhang, Y. (2018). Towards robust lung segmentation in chest radiographs with deep learning. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Z., Jiang, R., and Xie, Z. (2019, January 5\u20137). Instance segmentation of anatomical structures in chest radiographs. Proceedings of the IEEE 32nd International Symposium on Computer-Based Medical Systems, Corodoba, Spain.","DOI":"10.1109\/CBMS.2019.00092"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dong, N., Xu, M., Liang, X., Jiang, Y., Dai, W., and Xing, E. (2019, January 13\u201317). Neural architecture search for adversarial medical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32226-7_92"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1007\/s00521-017-3158-6","article-title":"Medical image semantic segmentation based on deep learning","volume":"29","author":"Jiang","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_50","unstructured":"(2020, January 16). X-RayNet Model with Algorithms. Available online: http:\/\/dm.dgu.edu\/link.html."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., and Funkhouser, T. (2017, January 21\u201326). Dilated residual networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Naqvi, R.A., Kim, D.S., Nguyen, P.H., Owais, M., and Park, K.R. (2018). IrisDenseNet: Robust iris segmentation using densely connected fully convolutional networks in the images by visible light and near-infrared light camera sensors. Sensors, 18.","DOI":"10.3390\/s18051501"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.eswa.2019.01.010","article-title":"FRED-Net: Fully residual encoder\u2013decoder network for accurate iris segmentation","volume":"122","author":"Arsalan","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Kim, D.S., Owais, M., and Park, K.R. (2020). OR-Skip-Net: Outer residual skip network for skin segmentation in non-ideal situations. Expert Syst. Appl., 141.","DOI":"10.1016\/j.eswa.2019.112922"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","article-title":"Development of a digital image database for chest radiographs with and without a lung nodule","volume":"174","author":"Shiraishi","year":"2000","journal-title":"Am. J. Roentgenol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.media.2005.02.002","article-title":"Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database","volume":"10","author":"Stegmann","year":"2006","journal-title":"Med. Image Anal."},{"key":"ref_60","unstructured":"(2019, December 04). GeForce GTX TITAN X Graphics Processing Unit. Available online: https:\/\/www.geforce.com\/hardware\/desktop-gpus\/geforce-gtx-titan-x\/specifications."},{"key":"ref_61","unstructured":"(2019, December 04). MATLAB R2019a. Available online: https:\/\/ch.mathworks.com\/products\/new_products\/latest_features.html."},{"key":"ref_62","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference for Learning Representations, San Diego, CA, USA."},{"key":"ref_63","first-page":"475","article-title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"Jaeger","year":"2014","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Vajda, S., Karargyris, A., Jaeger, S., Santosh, K.C., Candemir, S., Xue, Z., Antani, S., and Thoma, G. (2018). Feature selection for automatic tuberculosis screening in frontal chest radiographs. J. Med. Syst., 42.","DOI":"10.1007\/s10916-018-0991-9"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TMI.2017.2775636","article-title":"Automated chest X-ray screening: Can lung region symmetry help detect pulmonary abnormalities?","volume":"37","author":"Santosh","year":"2018","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/9\/3\/871\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:10:40Z","timestamp":1760173840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/9\/3\/871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,23]]},"references-count":65,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["jcm9030871"],"URL":"https:\/\/doi.org\/10.3390\/jcm9030871","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,23]]}}}