{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:47:01Z","timestamp":1782276421616,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Convolutional Neural Networks (CNNs) and advanced image pre-processing can enhance the classification of antioxidant effects applied on zebrafish intestine. This study proposes a hybrid technique that combines four deep learning (DL) models: the pre-trained Xception CNN, a custom-built autoencoder, a custom-built CNN, and a Vision Transformer (ViT). Methods: For classification features generated by DL models, the Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (kNN) algorithms were proposed. Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the mentioned DL artificial intelligence (AI) algorithms were applied to improve the accuracy of the classification of histological images of zebrafish intestinal morphology. Results: In a binary classification, the following classes were studied on zebrafish intestine: (i) control and experimental-induced oxidative stress (OS); (ii) OS versus OS and theobromine (TB); and (iii) OS versus OS and caffeine (CAF). The novelty of the research lies in applying CLAHE to enhance image quality and utilizing four hybrid models to improve classification accuracy compared to raw images, when a private dataset of zebrafish intestine histology under certain chemical treatments (OS, TB, CAF) was employed. Conclusions: The best results are obtained in a binary classification with a hybrid combination of Xception and SVM for OS versus OS and TB classes, with an accuracy of 84.6% for pre-processed images, better than raw images, when the accuracy was 78.4%.<\/jats:p>","DOI":"10.3390\/diagnostics15222859","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T07:58:32Z","timestamp":1762934312000},"page":"2859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid Deep Learning Models for Analyzing Histological Images of the Zebrafish Intestine Under Oxidative Stress"],"prefix":"10.3390","volume":"15","author":[{"given":"Cristian Dan","family":"Pavel","sequence":"first","affiliation":[{"name":"Department of Morphofunctional Sciences I, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-329X","authenticated-orcid":false,"given":"Simona","family":"Moldovanu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800146 Galati, Romania"},{"name":"The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1116-1585","authenticated-orcid":false,"given":"Irina Andreea","family":"Pavel","sequence":"additional","affiliation":[{"name":"Department of Surgery II, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, University Street, No.16, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oana-Maria","family":"Dragostin","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800201 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3212-7431","authenticated-orcid":false,"given":"Carmen Lidia","family":"Chi\u021bescu","sequence":"additional","affiliation":[{"name":"Research Centre in the Medical-Pharmaceutical Field, Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800201 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8431-6904","authenticated-orcid":false,"given":"Carmen L\u0103cr\u0103mioara","family":"Zamfir","sequence":"additional","affiliation":[{"name":"Department of Morphofunctional Sciences I, Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s00204-024-03696-4","article-title":"Several lines of antioxidant defense against oxidative stress: Antioxidant enzymes, nanomaterials with multiple enzyme-mimicking activities, and low-molecular-weight antioxidants","volume":"98","author":"Jomova","year":"2024","journal-title":"Arch. Toxicol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pavel, C.D., Gales, C., Pavel, I.A., and Zamfir, C.L. (2025). The Relevance of Experimental Models in Assessing the Impact of Oxidative Stress on Intestinal Pathology. J. Clin. Med., 14.","DOI":"10.3390\/jcm14186569"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1089\/zeb.2021.0083","article-title":"Use of Zebrafish as a Model Organism to Study Oxidative Stress: A Review","volume":"19","author":"Chowdhury","year":"2022","journal-title":"Zebrafish"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Batir-Marin, D., Boev, M., Cioanca, O., Lungu, I.-I., Marin, G.-A., Burlec, A.F., Mitran, A.M., Mircea, C., and Hancianu, M. (2025). Exploring Oxidative Stress Mechanisms of Nanoparticles Using Zebrafish (Danio rerio): Toxicological and Pharmaceutical Insights. Antioxidants, 14.","DOI":"10.20944\/preprints202503.1561.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1002\/jat.4842","article-title":"Oxidative Stress in Zebrafish Gut Induced by Individual and Combined Exposure to Amoxicillin, Arsenic and Fluoride: Engagement of Nrf2-Keap1-ARE Pathway","volume":"45","author":"Mukherjee","year":"2025","journal-title":"J. Appl. Toxicol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Boix, N., Teixido, E., Pique, E., Llobet, J.M., and Gomez-Catalan, J. (2020). Modulation and Protection Effects of Antioxidant Compounds against Oxidant Induced Developmental Toxicity in Zebrafish. Antioxidants, 9.","DOI":"10.3390\/antiox9080721"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abbate, F., Maugeri, A., Laur\u00e0, R., Levanti, M., Navarra, M., Cirmi, S., and German\u00e0, A. (2021). Zebrafish as a Useful Model to Study Oxidative Stress-Linked Disorders: Focus on Flavonoids. Antioxidants, 10.","DOI":"10.3390\/antiox10050668"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.saa.2019.03.001","article-title":"Study on the interaction and antioxidant activity of theophylline and theobromine with SOD by spectra and calculation","volume":"215","author":"Wu","year":"2019","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"\u0150sz, B.E., J\u00eetc\u0103, G., \u0218tef\u0103nescu, R.E., Pu\u0219ca\u0219, A., Tero-Vescan, A., and Vari, C.-E. (2022). Caffeine and Its Antioxidant Properties\u2014It Is All about Dose and Source. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms232113074"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rodak, K., Kokot, I., and Kratz, E.M. (2021). Caffeine as a Factor Influencing the Functioning of the Human Body-Friend or Foe?. Nutrients, 13.","DOI":"10.3390\/nu13093088"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1038\/s41573-021-00233-1","article-title":"Targeting oxidative stress in disease: Promise and limitations of antioxidant therapy","volume":"20","author":"Forman","year":"2021","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10120-023-01451-9","article-title":"Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: Development and validation study","volume":"27","author":"Fang","year":"2024","journal-title":"Gastric Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1007\/s10278-024-01021-0","article-title":"Classification of H. pylori Infection from Histopathological Images Using Deep Learning","volume":"37","author":"Ibrahim","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s00428-024-03740-9","article-title":"Deep learning model to differentiate Crohn\u2019s disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens","volume":"484","author":"Liu","year":"2024","journal-title":"Virchows Arch."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1093\/bfgp\/elad032","article-title":"Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning","volume":"23","author":"Li","year":"2024","journal-title":"Brief. Funct. Genom."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Feng, G., Kan, X., and Chen, M. (2024). A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behaviour Analysis Using Efficientnet. Appl. Sci., 14.","DOI":"10.3390\/app14125099"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Karimanzira, D. (2025). Comprehensive Fish Feeding Management in Pond Aquaculture Based on Fish Feeding Behavior Analysis Using a Vision Language Model. Aquac. J., 5.","DOI":"10.20944\/preprints202507.1070.v1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Joshi, S.A., Bongale, A.M., Olsson, P.O., Urolagin, S., Dharrao, D., and Bongale, A. (2023). Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection. Computation, 11.","DOI":"10.3390\/computation11030059"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Carreras, J., Roncador, G., and Hamoudi, R. (2024). Ulcerative Colitis, LAIR1 and TOX2 Expression, and Colorectal Cancer Deep Learning Image Classification Using Convolutional Neural Networks. Cancers, 16.","DOI":"10.20944\/preprints202411.0211.v1"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vanitha, K., Mahesh, T., Sree, S.S., and Guluwadi, S. (2014). Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02628-7"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Munteanu, D., Moldovanu, S., Tabacaru, G., and Barbu, M. (2025, January 10\u201313). Influence of Symmetric and Asymmetric CAE-CNN on Colon Cancer Histopathological Images Classification. Proceedings of the 33rd Mediterranean Conference on Control and Automation (MED), Tangier, Morocco.","DOI":"10.1109\/MED64031.2025.11073519"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.patcog.2018.09.007","article-title":"Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images","volume":"86","author":"Hou","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Atabansi, C.C., Nie, J., Liu, H., Song, Q., Yan, L., and Zhou, X. (2023). A survey of Transformer applications for histopathological image analysis: New developments and future directions. Biomed. Eng. Online, 22.","DOI":"10.1186\/s12938-023-01157-0"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rahmani, A.M., Yousefpoor, E., Yousefpoor, M.S., Mehmood, Z., Haider, A., Hosseinzadeh, M., and Ali Naqvi, R. (2021). Machine Learning (ML) in Medicine: Review, Applications, and Challenges. Mathematics, 9.","DOI":"10.3390\/math9222970"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hamdi, M., Senan, E.M., Jadhav, M.E., Olayah, F., Awaji, B., and Alalayah, K.M. (2023). Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas. Diagnostics, 13.","DOI":"10.3390\/diagnostics13132258"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"87560","DOI":"10.1109\/ACCESS.2024.3415482","article-title":"Automated identification of breast Cancer type using novel multipath transfer learning and ensemble of classifier","volume":"12","author":"Salini","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hussein, F., Mughaid, A., AlZu\u2019bi, S., El-Salhi, S.M., Abuhaija, B., Abualigah, L., and Gandomi, A.H. (2022). Hybrid clahe-cnn deep neural networks for classifying lung diseases from X-ray acquisitions. Electronics, 11.","DOI":"10.3390\/electronics11193075"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11554-024-01465-1","article-title":"Adaptive histogram equalization in constant time","volume":"21","author":"Steger","year":"2024","journal-title":"J. Real-Time Image Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100231","DOI":"10.1016\/j.imu.2019.100231","article-title":"Cancer diagnosis in histopathological image: CNN based approach","volume":"16","author":"Dabeer","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2022004","DOI":"10.6703\/IJASE.202303_20(1).006","article-title":"Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and CLAHE framework","volume":"20","author":"Hadiyoso","year":"2023","journal-title":"Int. J. Appl. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.procs.2022.12.111","article-title":"Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning","volume":"216","author":"Hayati","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"28493","DOI":"10.1007\/s11042-024-20289-6","article-title":"Cnn models aided with a metaclassifier for lung Carcinoma classification using histopathological images","volume":"84","author":"Gautam","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100126","DOI":"10.1016\/j.ibmed.2023.100126","article-title":"Feed-Forward Networks Using Logistic Regression and Support Vector Machine for Whole-Slide Breast Cancer Histopathology Image Classification","volume":"9","author":"Karuppasamy","year":"2024","journal-title":"Intell. Based Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Stefano, A., Bini, F., Giovagnoli, E., Dimarco, M., Lauciello, N., Narbonese, D., Pasini, G., Marinozzi, F., Russo, G., and D\u2019Angelo, I. (2025). Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography. Diagnostics, 15.","DOI":"10.20944\/preprints202503.0184.v1"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/22\/2859\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T08:01:32Z","timestamp":1762934492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/22\/2859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,12]]},"references-count":35,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["diagnostics15222859"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15222859","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,12]]}}}