{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T03:11:33Z","timestamp":1785553893616,"version":"3.56.0"},"reference-count":16,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,2,20]]},"abstract":"<jats:p>Tuberculosis (TB) is an airborne infection affected by Mycobacterium TB. It is vital to identify cases of TB quickly if left untreated; there exists a 70% possibility of a patient dying in 10 years. An essential for extra device has been enhanced in mid to low-income countries because of the growth of automation in the field of medical care. The already restricted resources are being greatly assigned to control other dangerous infections. Modern digital radiography (DR) machines, utilized to screen chest X-rays (CXR) of possible TB victims. Combined with computer-aided detection (CAD) with the support of artificial intelligence (AI), radiologists employed in this domain actual support possible cases. This study presents a Hybrid Deep Learning Assisted Chest X-Ray Image Segmentation and Classification for Tuberculosis (HDL-ISCTB) diagnosis. The HDL-ISCTB model performs Otsu\u2019s thresholding, which segments the lung regions from the input images. It effectually discriminates the lung areas from the background, decreasing computational complexity and potential noise. Besides, the segmented lung regions are then fed into the CNN-LSTM architecture for classification. The CNN-LSTM model leverages the powerful feature extraction capabilities of CNNs and the temporal dependencies captured by LSTM to obtain robust representations from sequential CXR image data. A wide experiments are conducted to calculate the performance of the presented approach in comparison to recent methods.<\/jats:p>","DOI":"10.3233\/idt-230286","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T11:24:08Z","timestamp":1702380248000},"page":"561-569","source":"Crossref","is-referenced-by-count":4,"title":["Hybrid deep learning assisted chest X-ray image segmentation and classification for tuberculosis disease diagnosis"],"prefix":"10.1177","volume":"18","author":[{"given":"Ajay","family":"Tiwari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alok","family":"Katiyar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-230286_ref1","first-page":"1","article-title":"A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images","author":"Ravi","year":"2022","journal-title":"Cluster Computing"},{"key":"10.3233\/IDT-230286_ref2","doi-asserted-by":"crossref","first-page":"102234","DOI":"10.1016\/j.tube.2022.102234","article-title":"An efficient deep learning-based framework for tuberculosis detection using chest X-ray images","volume":"136","author":"Iqbal","year":"2022","journal-title":"Tuberculosis."},{"issue":"1","key":"10.3233\/IDT-230286_ref3","doi-asserted-by":"crossref","first-page":"118","DOI":"10.5114\/pjr.2022.113435","article-title":"Deep learning-based automatic detection of tuberculosis disease in chest X-ray images","volume":"87","author":"Showkatian","year":"2022","journal-title":"Polish Journal of Radiology."},{"issue":"1","key":"10.3233\/IDT-230286_ref4","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s13246-020-00966-0","article-title":"Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors","volume":"44","author":"Ayaz","year":"2021","journal-title":"Physical and Engineering Sciences in Medicine."},{"key":"10.3233\/IDT-230286_ref5","doi-asserted-by":"crossref","first-page":"103632","DOI":"10.1109\/ACCESS.2022.3208882","article-title":"Stochastic Learning-Based Artificial Neural Network Model for an Automatic Tuberculosis Detection System Using Chest X-Ray Images","volume":"10","author":"Urooj","year":"2022","journal-title":"IEEE Access."},{"key":"10.3233\/IDT-230286_ref6","doi-asserted-by":"crossref","unstructured":"Acharya V, Dhiman G, Prakasha K, Bahadur P, Choraria A, Prabhu S, Chadaga K, Viriyasitavat W, Kautish S. AI-assisted tuberculosis detection and classification from chest X-rays using a deep learning normalization-free network model. Computational Intelligence and Neuroscience, 2022.","DOI":"10.1155\/2022\/2399428"},{"issue":"11","key":"10.3233\/IDT-230286_ref7","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s10916-022-01870-8","article-title":"Advances in Deep Learning for Tuberculosis Screening Using Chest X-Rays: The Last 5 Years Review","volume":"46","author":"Santosh","year":"2022","journal-title":"Journal of Medical Systems."},{"key":"10.3233\/IDT-230286_ref8","first-page":"5","article-title":"TB-Net: a tailored, self-attention deep convolutional neural network design for detection of tuberculosis cases from chest X-ray images","author":"Wong","year":"2022","journal-title":"Frontiers in Artificial Intelligence."},{"issue":"2","key":"10.3233\/IDT-230286_ref9","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1148\/radiol.2021210063","article-title":"Deep learning to determine the activity of pulmonary tuberculosis on chest radiographs","volume":"301","author":"Lee","year":"2021","journal-title":"Radiology."},{"issue":"5","key":"10.3233\/IDT-230286_ref10","doi-asserted-by":"crossref","first-page":"785","DOI":"10.3233\/XST-210894","article-title":"Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings","volume":"29","author":"Nijiati","year":"2021","journal-title":"Journal of X-ray Science and Technology."},{"key":"10.3233\/IDT-230286_ref11","doi-asserted-by":"crossref","unstructured":"Capell\u00e1n-Mart\u00edn D, G\u00f3mez-Valverde JJ, Sanchez-Jacob R, Bermejo-Pel\u00e1ez D, Garc\u00eda-Delgado L, L\u00f3pez-Varela E, Ledesma-Carbayo MJ. Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis, 2023. arXiv preprint arXiv2301.13786.","DOI":"10.1117\/12.2652626"},{"issue":"16","key":"10.3233\/IDT-230286_ref12","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.3390\/electronics11162514","article-title":"A Perceptual Encryption-Based Image Communication System for Deep Learning-Based Tuberculosis Diagnosis Using Healthcare Cloud Services","volume":"11","author":"Ahmad","year":"2022","journal-title":"Electronics."},{"key":"10.3233\/IDT-230286_ref13","first-page":"1","article-title":"ViT-TB: Ensemble Learning Based ViT Model for Tuberculosis Recognition","author":"Ammar","year":"2022","journal-title":"Cybernetics and Systems"},{"key":"10.3233\/IDT-230286_ref14","doi-asserted-by":"crossref","first-page":"108094","DOI":"10.1016\/j.asoc.2021.108094","article-title":"An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images","volume":"114","author":"Dey","year":"2022","journal-title":"Applied Soft Computing."},{"key":"10.3233\/IDT-230286_ref17","doi-asserted-by":"crossref","unstructured":"Orosoo M, Govindasamy S, Bayarsaikhan N, Rajkumari Y, Fatma G, Manikandan R, Bala BK. Performance analysis of a novel hybrid deep learning approach in classification of quality-related English text. Measurement: Sensors, 2023, p.\u00a0100852.","DOI":"10.1016\/j.measen.2023.100852"},{"key":"10.3233\/IDT-230286_ref18","doi-asserted-by":"crossref","unstructured":"Orosoo M, Govindasamy S, Bayarsaikhan N, Rajkumari Y, Fatma G, Manikandan R, Bala BK. Performance analysis of a novel hybrid deep learning approach in classification of quality-related English text. Measurement: Sensors, 2023, p.\u00a0100852.","DOI":"10.1016\/j.measen.2023.100852"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-230286","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:23:47Z","timestamp":1777454627000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-230286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":16,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/idt-230286","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,20]]}}}