DeepGram: Transfer Learning for Gram-Stain Bacterial Species Prediction.
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| Title: | DeepGram: Transfer Learning for Gram-Stain Bacterial Species Prediction. |
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| Authors: | Akbar, Son Ali1 sonali@ee.uad.ac.id, Sunardi2 sunardi@te.uad.ac.id, Ghazali, Kamarul Hawari3 kamarul@umpsa.edu.my, Rosyady, Phisca Aditya4 phisca.aditya@te.uad.ac.id, Mardhia, Murein Miksa5 murein.miksa@tif.uad.ac.id, Muda, Razali6 razali@umpsa.edu.my, Najib, Muhammad Sharfi7 sharfi@umpsa.edu.my |
| Source: | Engineering Letters. Apr2026, Vol. 34 Issue 4, p1122-1130. 9p. |
| Subjects: | Gram's stain, Bacteria classification, Machine learning, Bloodborne infections, Drug resistance in microorganisms, Deep learning, Data augmentation |
| Abstract: | Bacterial bloodstream infections (BSIs) are a serious global health issue, often leading to high mortality rates due to delays in diagnosis and the growing challenge of antimicrobial resistance. To address this, deep learning-based computer-aided diagnosis has emerged as a promising tool for predicting bacterial infections. This study focuses on classifying gram-stain bacterial species directly, leveraging the Xception model by using transfer learning approach combined with image augmentation techniques to reduce misclassification on prediction performance. The proposed model achieves an accuracy of 98.78%, outperforming existing methods while also reducing the false positive rate (FPR) by 0.22%. Therefore, this architecture demonstrates remarkable effectiveness in analyzing fine-grained textures and morphological features of bacteria, making it particularly suitable for detailed microbiological diagnostics. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192720677 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DeepGram: Transfer Learning for Gram-Stain Bacterial Species Prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akbar%2C+Son+Ali%22">Akbar, Son Ali</searchLink><relatesTo>1</relatesTo><i> sonali@ee.uad.ac.id</i><br /><searchLink fieldCode="AR" term="%22Sunardi%22">Sunardi</searchLink><relatesTo>2</relatesTo><i> sunardi@te.uad.ac.id</i><br /><searchLink fieldCode="AR" term="%22Ghazali%2C+Kamarul+Hawari%22">Ghazali, Kamarul Hawari</searchLink><relatesTo>3</relatesTo><i> kamarul@umpsa.edu.my</i><br /><searchLink fieldCode="AR" term="%22Rosyady%2C+Phisca+Aditya%22">Rosyady, Phisca Aditya</searchLink><relatesTo>4</relatesTo><i> phisca.aditya@te.uad.ac.id</i><br /><searchLink fieldCode="AR" term="%22Mardhia%2C+Murein+Miksa%22">Mardhia, Murein Miksa</searchLink><relatesTo>5</relatesTo><i> murein.miksa@tif.uad.ac.id</i><br /><searchLink fieldCode="AR" term="%22Muda%2C+Razali%22">Muda, Razali</searchLink><relatesTo>6</relatesTo><i> razali@umpsa.edu.my</i><br /><searchLink fieldCode="AR" term="%22Najib%2C+Muhammad+Sharfi%22">Najib, Muhammad Sharfi</searchLink><relatesTo>7</relatesTo><i> sharfi@umpsa.edu.my</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Apr2026, Vol. 34 Issue 4, p1122-1130. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gram's+stain%22">Gram's stain</searchLink><br /><searchLink fieldCode="DE" term="%22Bacteria+classification%22">Bacteria classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bloodborne+infections%22">Bloodborne infections</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+resistance+in+microorganisms%22">Drug resistance in microorganisms</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Bacterial bloodstream infections (BSIs) are a serious global health issue, often leading to high mortality rates due to delays in diagnosis and the growing challenge of antimicrobial resistance. To address this, deep learning-based computer-aided diagnosis has emerged as a promising tool for predicting bacterial infections. This study focuses on classifying gram-stain bacterial species directly, leveraging the Xception model by using transfer learning approach combined with image augmentation techniques to reduce misclassification on prediction performance. The proposed model achieves an accuracy of 98.78%, outperforming existing methods while also reducing the false positive rate (FPR) by 0.22%. Therefore, this architecture demonstrates remarkable effectiveness in analyzing fine-grained textures and morphological features of bacteria, making it particularly suitable for detailed microbiological diagnostics. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1122 Subjects: – SubjectFull: Gram's stain Type: general – SubjectFull: Bacteria classification Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Bloodborne infections Type: general – SubjectFull: Drug resistance in microorganisms Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Data augmentation Type: general Titles: – TitleFull: DeepGram: Transfer Learning for Gram-Stain Bacterial Species Prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akbar, Son Ali – PersonEntity: Name: NameFull: Sunardi – PersonEntity: Name: NameFull: Ghazali, Kamarul Hawari – PersonEntity: Name: NameFull: Rosyady, Phisca Aditya – PersonEntity: Name: NameFull: Mardhia, Murein Miksa – PersonEntity: Name: NameFull: Muda, Razali – PersonEntity: Name: NameFull: Najib, Muhammad Sharfi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 4 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
