Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry.
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| Title: | Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry. |
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| Authors: | Sayed, Gehad Ismail1,2 (AUTHOR) gehad_sayed@cic-cairo.com, Hassanien, Aboul Ella2,3 (AUTHOR) aboitcairo@cu.edu.eg, Basha, Sameh H.2,4,5 (AUTHOR) SamehBasha@cu.edu.eg |
| Source: | Environment, Development & Sustainability. Jun2026, Vol. 28 Issue 6, p14941-14968. 28p. |
| Subject Terms: | *Neutrosophic logic, *Deep learning, *Avian coccidiosis, *Classification, *Poultry industry, *Optimization algorithms, *Image enhancement (Imaging systems) |
| Abstract: | To optimize infection control and bolster productivity within the poultry industry, it is imperative to accurately classify Chicken Eimeria species. There are several methods for determining Eimeria disease in chickens. Traditional methods involve watching for clinical symptoms, and macroscopic lesions, and studying the parasite's biology and oocyst morphology. These methods are frequently time-consuming and labor-intensive, necessitating the manual collection and analysis of samples, which can be especially difficult in large chicken farms. Deep learning algorithms, on the other hand, provide automated, accurate, and non-invasive methods for the detection of Eimeria. This paper proposed a classification model for the automatic classification of chicken Eimeria species. The proposed model is mainly based on integrating neutrosophic set theory and InceptionV3 deep-learning architecture. Three primary phases make up the proposed chicken Eimeria species classification model: the data preprocessing phase, the neutrosophic image conversion phase, and the image classification phase. To address the issue of class imbalance in the adopted dataset and enhance the model's generalizability, the random oversampling method, and data augmentation techniques are employed during the data preprocessing phase. The preprocessed data is considered to feed the neutrosophic set-based segmentation algorithm, where true, false, and intermediate subsets are extracted. Finally, the true subset is utilized to feed the optimized InceptionV3. To determine the optimal hyperparameter values for InceptionV3, a modified version of the Brown Bear optimization algorithm is proposed in this paper. To evaluate the effectiveness of the proposed model, a real benchmark dataset comprising images of different Eimeria species is adopted. The experimental results revealed that the proposed model offers a more efficient and accurate alternative to traditional methods and state-of-the-art models, enabling faster and more effective diagnosis and treatment of Eimeria infections. It achieved an overall accuracy, specificity, sensitivity, and F1-score of nearly 100%. Additionally, the results showed that the high performance of the proposed model can reduce labor costs and boost throughput, thereby enhancing economic viability even more. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194093148 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sayed%2C+Gehad+Ismail%22">Sayed, Gehad Ismail</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gehad_sayed@cic-cairo.com</i><br /><searchLink fieldCode="AR" term="%22Hassanien%2C+Aboul+Ella%22">Hassanien, Aboul Ella</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> aboitcairo@cu.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Basha%2C+Sameh+H%2E%22">Basha, Sameh H.</searchLink><relatesTo>2,4,5</relatesTo> (AUTHOR)<i> SamehBasha@cu.edu.eg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jun2026, Vol. 28 Issue 6, p14941-14968. 28p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Neutrosophic+logic%22">Neutrosophic logic</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Avian+coccidiosis%22">Avian coccidiosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br />*<searchLink fieldCode="DE" term="%22Poultry+industry%22">Poultry industry</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To optimize infection control and bolster productivity within the poultry industry, it is imperative to accurately classify Chicken Eimeria species. There are several methods for determining Eimeria disease in chickens. Traditional methods involve watching for clinical symptoms, and macroscopic lesions, and studying the parasite's biology and oocyst morphology. These methods are frequently time-consuming and labor-intensive, necessitating the manual collection and analysis of samples, which can be especially difficult in large chicken farms. Deep learning algorithms, on the other hand, provide automated, accurate, and non-invasive methods for the detection of Eimeria. This paper proposed a classification model for the automatic classification of chicken Eimeria species. The proposed model is mainly based on integrating neutrosophic set theory and InceptionV3 deep-learning architecture. Three primary phases make up the proposed chicken Eimeria species classification model: the data preprocessing phase, the neutrosophic image conversion phase, and the image classification phase. To address the issue of class imbalance in the adopted dataset and enhance the model's generalizability, the random oversampling method, and data augmentation techniques are employed during the data preprocessing phase. The preprocessed data is considered to feed the neutrosophic set-based segmentation algorithm, where true, false, and intermediate subsets are extracted. Finally, the true subset is utilized to feed the optimized InceptionV3. To determine the optimal hyperparameter values for InceptionV3, a modified version of the Brown Bear optimization algorithm is proposed in this paper. To evaluate the effectiveness of the proposed model, a real benchmark dataset comprising images of different Eimeria species is adopted. The experimental results revealed that the proposed model offers a more efficient and accurate alternative to traditional methods and state-of-the-art models, enabling faster and more effective diagnosis and treatment of Eimeria infections. It achieved an overall accuracy, specificity, sensitivity, and F1-score of nearly 100%. Additionally, the results showed that the high performance of the proposed model can reduce labor costs and boost throughput, thereby enhancing economic viability even more. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-024-05478-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 14941 Subjects: – SubjectFull: Neutrosophic logic Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Avian coccidiosis Type: general – SubjectFull: Classification Type: general – SubjectFull: Poultry industry Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general Titles: – TitleFull: Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sayed, Gehad Ismail – PersonEntity: Name: NameFull: Hassanien, Aboul Ella – PersonEntity: Name: NameFull: Basha, Sameh H. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 28 – Type: issue Value: 6 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
