Kernel Metadata Declaration — on demand

What the DOI identifies

A CONTEMPORARY TECHNIQUE FOR LUNG DISEASE PREDICTION USING MACHINE LEARNING

10.46243/jst.2023.v8.i06.pp101-109 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-08-07

Part of Journal of Science & Technology · ISSN 2456-5660 · pp. 101–109

Principal agents

• Dr. L. MALLIGA (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 3 links, 10 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder– decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods",
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            "unstructured": "Ch.Venkateswarlu, K.Vennela, G.Manju Bhargavi, J.Sai Sravani4, Iot Based Smart Agriculture Monitoring System, Journal Of Applied Science And Computations, Issn No: 1076-5131, Volume Ix, Issue Xi, November/2022, Pg127-1220"
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            "unstructured": "CH. Venkateswarllu, R. Sowjanya, P. Sai Asritha, V. Keerthi, Benign and Malignant Skin Cancer Detection using Probabilistic Neural Network, Journal of Interdisciplinary Cycle Research, ISSN NO: 0022-1945, Volume XIV, Issue XI, November/2022, Pg 808-819"
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            "unstructured": "International Vaccine Access Center Johns Hopkins Bloomberg School of Public Health, Pneumonia and Diarrhea Progress Report 2020, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA, 2020"
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            "key": "ref4",
            "doi": "10.1146/annurev-bioeng-071516-044442",
            "unstructured": "D. Shen, G. Wu, and H. I. Suk, ―Deep learning in medical image analysis,‖ Annual Review of Biomedical Engineering, vol. 19, no. 1, pp. 221–248, 2017"
        },
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            "key": "ref5",
            "doi": "10.1007/s11684-019-0726-4",
            "unstructured": "J. Ma, Y. Song, X. Tian, Y. Hua, R. Zhang, and J. Wu, ―Survey on deep learning for pulmonary medical imaging,‖ Frontiers of Medicine, vol. 14, pp. 450–469, 2020"
        },
        {
            "key": "ref6",
            "doi": "10.3390/diagnostics10090649",
            "unstructured": "N. M. Elshennawy and D. M. Ibrahim, ―Deeppneumonia framework using deep learning models based on chest X-ray images,‖ Diagnostics, vol. 10, no. 9, p. 649, 2020"
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            "key": "ref7",
            "doi": "10.1109/tmi.2020.2995508",
            "unstructured": "Xi Ouyang, J. Huo, L. Xia et al., ―Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia,‖ IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2595–2605, 2020"
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            "unstructured": "M. M. Ahsan, T. E. Alam, T. Trafalis, and P. Huebner, ―Deep MLP-CNN model using mixeddata to distinguish between COVID-19 and non-COVID-19 patients,‖ Symmetry, vol. 12, no. 9, p. 1526, 2020"
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            "doi": "10.1007/s11277-020-07732-1",
            "unstructured": "A. Naik and D. R. Edla, ―Lung nodule classification on computed tomography images using deep learning,‖ Wireless Personal Communications, vol. 116, pp. 655–690, 2021"
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            "doi": "10.1016/j.bspc.2022.103973",
            "unstructured": "M. Kanipriya, C. Hemalatha, N. Sridevi, S. SriVidhya, and S. Jany Shabu, ―An improved capuchin search algorithm optimized hybrid CNN-LSTM architecture for malignant lung nodule detection,‖ Biomedical Signal Processing and Control, vol. 78, no. 2022, Article ID 103973, 2022"
        }
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The Kernel Metadata Declaration

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