Assessing Lung Injury Induced by Streptozotocin-induced Diabetes: A Deep Neural Network Analysis of Histopathological and Immunohistochemical Images

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dc.contributor.authorSenturk, Tugba
dc.contributor.authorBolat, Demet
dc.contributor.authorYay, Arzu Hanim
dc.contributor.authorBaran, Munevver
dc.contributor.authorLatifoglu, Fatma
dc.date.accessioned2026-04-04T13:32:56Z
dc.date.available2026-04-04T13:32:56Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractIntroduction Diabetes mellitus is an endocrine disorder characterized by metabolic abnormalities and chronic hyperglycemia, caused by insulin deficiency (Type I) or resistance (Type II). It affects various tissues differently, and its complications extend beyond classical targets, such as the kidneys and eyes, to lesser-studied organs, including the lungs. Understanding tissue-specific damage is crucial for effective disease management and the prevention of complications.Objective This study aims to evaluate the histopathological and immunohistochemical effects of diabetic lung fibrosis using a streptozotocin (STZ)-induced diabetes model. Additionally, it seeks to develop a high-performance image classification system based on deep neural networks to accurately classify tissue damage in diabetic models.Methods Lung tissue samples were collected from the STZ-induced diabetes model and analyzed through histopathological and immunohistochemical techniques. Image data were further processed using convolutional neural networks (CNNs), including pre-trained models, such as ResNet50, VGG16, and SqueezeNet. Classification was conducted in multiple color spaces (RGB, Grayscale, and HSV) and evaluated using performance metrics, including confusion matrix, precision, recall, F1 score, and accuracy.Results The use of color significantly enhanced image patch classification performance. Among the models tested, SqueezeNet in the RGB color space demonstrated the highest accuracy, achieving an F1 score of 93.49% +/- 0.04 and an accuracy of 93.77% +/- 0.04. These results indicated the efficacy of CNN-based classification in detecting lung damage associated with diabetes.Discussion and Conclusion Our findings confirmed that diabetes induces histopathological changes in lung tissue, contributing to fibrosis and potential pulmonary complications. Deep learning-based classification methods, particularly when utilizing color space variations and advanced preprocessing techniques, provide a powerful tool for analyzing diabetic tissue damage and may aid in the development of diagnostic support systems.
dc.description.sponsorshipErciyes University Scientific Research Projects Coordination [THD-2019-8889]
dc.description.sponsorshipThis work was supported by Erciyes University Scientific Research Projects Coordination (Project number: THD-2019-8889) (Animals were obtained as a paid service within this project).
dc.identifier.doi10.2174/0115734099387481250930073924
dc.identifier.issn1573-4099
dc.identifier.issn1875-6697
dc.identifier.pmid41126428
dc.identifier.scopus2-s2.0-105023308841
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.2174/0115734099387481250930073924
dc.identifier.urihttps://hdl.handle.net/11616/108803
dc.identifier.wosWOS:001626971500001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBentham Science Publ Ltd
dc.relation.ispartofCurrent Computer-Aided Drug Design
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectConvolutional neural networks
dc.subjectimage classification
dc.subjectcolor space conversion
dc.subjectdiabetes
dc.subjecthistopathology
dc.subjectdeep learning
dc.titleAssessing Lung Injury Induced by Streptozotocin-induced Diabetes: A Deep Neural Network Analysis of Histopathological and Immunohistochemical Images
dc.typeArticle

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