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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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What the DOI identifies

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.

Licence https://creativecommons.org/licenses/by/4.0/

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.

ElementValueIn the record
DOI Name
DOI name
10.46243/jst.2021.v6.i05.pp153-158doi
Referent Type
referentType
Creationreferent
Referent Sub-Type
referentSubType
JournalArticle — an article in a journaltype
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 DOIidentifiers, relations (IsSameAs)
Registration Authority
registrationAuthorityCode
Crossref — issued by Crossref (member 25296); held here as a copyrecord.source_agency (our code, ra_doi_name, for names issued here once appointed)
Created Date
issueDate
2026-09-18record.registered (when the DOI name was first registered)
relatedIdentifiersnone needed — the descriptive metadata is in this recordcontainer, 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.

The System Metadata Declaration (JSON) · the Kernel Metadata Declaration (XML) · what each sub-type needs

History — the ledger

Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.

#WhenWhatByChanges
129 Sep 2026, 10:00 PMregister
registered at Crossref; record read from api.crossref.org
Administrator (admin) 90 fields set · sha256 d13cded6e3e9…
230 Sep 2026, 12:00 AMupdate
record re-read from api.crossref.org
Administrator (admin)
container.titles.0.value: Journal of Science & Technology → Journal of Science & Technology

Machine-readable: the history as JSON, with the full record after each change.

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