10.46243/jst.2021.v6.i05.pp153-158 registered
COMPARITIVE ANALYSIS OF LUNG DISEASE DETECTION USING DEEP LEARNING MODELS
Resolves to https://www.jst.org.in/index.php/pub/article/view/627
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2021.v6.i05.pp153-158
Registered 29 Sep 2026 via crossref · record version 2 · last change 30 Sep 2026, 12:00 AM · record sha256 5649b4102755bab5…
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JournalArticle — an article in a journal · Digital · Visual · en
COMPARITIVE ANALYSIS OF LUNG DISEASE DETECTION USING DEEP LEARNING MODELS (PrincipalTitle)
Published 2021-09-01
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 06 · issue 05 · pages 153–158
Agents
- Dr.Syed Abdul Sattar (author)
- d Khaleel Ahmed (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2021.v6.i05.pp153-158
Abstract
Now a days for identifying or predict any diseases on human beings, we should have proper diagnosis for predicting the disease which is present in that human body. In general for prediction of diseases we try to use either X-Ray, CT or MRI scan techniques for taking decision on that appropriate disease. In general medical person need complete knowledge on that appropriate domain to find out the abnormality which is present in human beings. As we all know that India tops the world for having more deaths due to lung diseases. After the second highest cause of deaths in India due to heart disease, this ling disease is one which is increasing its rank more and more. In order to reduce that problem early diagnosis and treatment of lung diseases is critical to prevent complications including death. Normally for finding the abnormality present in lung, chest X-ray is playing very important role to detect the complete information about the lungs. In this current article we try to present an effective way for expert diagnosis of lung diseases using deep learning models. It focuses on creating a system for assistance of Radiologists in detection of lung diseases. This will especially benefit rural areas where radiologists aren’t easily available. We use two models like Vgg16 and Vgg19 for predicting the lung disease from chest X ray images and then tell which model gives high accuracy and performance. We conclude by discussing research obstacles, emerging trends, and possible future directions for improving some more advancement.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2021.v6.i05.pp153-158 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | COMPARITIVE ANALYSIS OF LUNG DISEASE DETECTION USING DEEP LEARNING MODELS (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Dr.Syed Abdul Sattar author: d Khaleel Ahmed publisher: Longman Publishers published: 2021-09-01 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 05 · pp. 153–158 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2026-09-18 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
complete Every System Metadata element is here, with the basic metadata a journal article needs.
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History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 90 fields set · sha256 d13cded6e3e9… |
| 2 | 30 Sep 2026, 12:00 AM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
Machine-readable: the history as JSON, with the full record after each change.
