{"id":3093,"date":"2025-09-04T01:01:47","date_gmt":"2025-09-04T01:01:47","guid":{"rendered":"https:\/\/www.ijmhr.org\/IntJAnatRes\/?page_id=3093"},"modified":"2025-09-04T01:01:47","modified_gmt":"2025-09-04T01:01:47","slug":"ijar-2025-201","status":"publish","type":"page","link":"https:\/\/www.ijmhr.org\/IntJAnatRes\/ijar-2025-201","title":{"rendered":"IJAR.2025.201"},"content":{"rendered":"
<\/i> DOWNLOAD PDF<\/span><\/a><\/div><\/div><\/div>
<\/i> Table of Contents<\/span><\/a><\/div><\/div><\/div><\/div>\n

Type of Article:<\/strong> \u00a0Original Research<\/h3>\n

Volume 13; Issue 3 (September 2025)<\/strong><\/h3>\n

Page No.:<\/strong> 9306-9313<\/h3>\n

DOI:\u00a0<\/strong>https:\/\/dx.doi.org\/10.16965\/ijar.2025.201<\/h3>\n

Automated Classification of Normal Histological Tissues Using Convolution neural network: A ResNet-50-Based Educational Tool<\/h3>\n

Haripriya 1<\/sup>, Sharad Gavhane 2<\/sup>, K.Vijayakumar *3<\/sup>.<\/strong><\/h3>\n

1<\/sup> Associate Professor, Department of Anatomy, Sri Ramachandra Medical College & Research Institute (SRMC & RI), Sri Ramachandra Institute for Higher Education and Research (SRIHER), Chennai, Tamil Nadu, India. ORCiD: <\/strong>https:\/\/orcid.org\/0000-0001-6136-7576<\/p>\n

2<\/sup> Medical officer Department of General Medicine, Symbiosis Medical College and hospital (SUHRC), Symbiosis International (Deemed) University (SIU) Pune, Maharashtra, India. ORCiD: <\/strong>https:\/\/orcid.org\/0009-0001-6627-4560<\/p>\n

*3 <\/sup>Assistant Professor, Department of Anatomy, Symbiosis Medical College for Women & Symbiosis University Hospital and Research Centre, Symbiosis International (Deemed University), Pune, India. ORCiD: <\/strong>https:\/\/orcid.org\/0000-0003-3032-8974<\/p>\n

Corresponding Author:<\/strong> Dr. \u00a0K. Vijayakumar, Assistant Professor, Department of Anatomy, Symbiosis Medical College for Women & Symbiosis University Hospital and Research Centre, Symbiosis International (Deemed University), Pune, India. Mobile: 9940695046 E-Mail:<\/strong> kvijay.india@gmail.com<\/p>\n

ABSTRACT<\/strong><\/p>\n

Background:<\/strong> The process of identifying histological slides is an essential part of medical education and pathology. AI tools can perform a decent job in radiology and dermatopathology, but the gap for fully automated, multi-class histological tissue classification is significant for teaching and learning purposes.<\/p>\n

Aim: <\/strong>To create and validate a deep learning system developed on 13 categories of normal human fine tissue using Convolutional Neural Networks (CNN) to aid in extended histology education and non-diagnostic digital pathology.<\/p>\n

Methods: <\/strong>This is a developmental diagnostic accuracy study with an experimental computational design. A total of 3250 high-resolution, expert-annotated images depicting 13 different normal histological tissue types. Training (70%), validation (15%), and testing (15%) sets were split from the dataset. The remaining methods fine-tuned three CNN architectures: ResNet-50, \u2002EfficientNet-B0, and MobileNetV2.<\/p>\n

Results:<\/strong> The performance results ranking for ResNet-50 was better overall among the three models, with an overall accuracy, precision, recall, and F1-score of 94. 2%, 93. 8%, 93. 5%, and 93. 6% in the test set, respectively. The accuracies of EfficientNet-B0 and MobileNetV2 were 91.7% and 89.3%, respectively.<\/p>\n

Conclusion:<\/strong> The proposed ResNet-50-based CNN model has high accuracy in diagnosing normal histological tissues and can be a useful training tool for medical and paramedical students. This study fills a crucial void by offering an automated, scalable, and explainable system for histological image classification, with a high potential for integration into digital learning devices.<\/p>\n

Keywords:<\/strong> Histology, Deep Learning, ResNet-50, Convolutional Neural Networks, Digital Pathology, Medical Education.<\/strong><\/p>\n

REFERENCES<\/strong><\/p>\n

[1]. Ball CS. The Early History of the Compound Microscope. Bios. 1966;37(2):51-60.
[2]. Wills M. The Evolution of the Microscope [Internet]. JSTOR Daily. 2018.
[3]. Pallua JD, Brunner A, Zelger B, Schirmer M, Haybaeck J. The future of pathology is digital. Pathol Res Pract. 2020 Sep;216(9):153040.
https:\/\/doi.org\/10.1016\/j.prp.2020.153040
PMid:32825928
[4]. Al-Janabi S, Huisman A, Van Diest PJ. Digital pathology: current status and future perspectives. Histopathology. 2012;61(1):1-9.
https:\/\/doi.org\/10.1111\/j.1365-2559.2011.03814.x
PMid:21477260
[5]. Ahmed AA, Abouzid M, Kaczmarek E. Deep Learning Approaches in Histopathology. Cancers. 2022 Oct 26;14(21):5264.
https:\/\/doi.org\/10.3390\/cancers14215264
PMid:36358683 PMCid:PMC9654172
[6]. El-Sherif DM, Abouzid M, Elzarif MT, Ahmed AA, Albakri A, Alshehri MM. Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic. Healthcare. 2022 Feb 18;10(2):385.
https:\/\/doi.org\/10.3390\/healthcare10020385
PMid:35206998 PMCid:PMC8871559
[7]. Fuchs TJ, Buhmann JM. Computational pathology: challenges and promises for tissue analysis. Comput Med Imaging Graph. 2011;35(7-8):515-30.
https:\/\/doi.org\/10.1016\/j.compmedimag.2011.02.006
PMid:21481567
[8]. Louis DN, Feldman M, Carter AB, Dighe AS, Pfeifer JD, Bry L, et al. Computational Pathology. Arch Pathol Lab Med. 2016 Jan;140(1):41-50.
https:\/\/doi.org\/10.5858\/arpa.2015-0093-SA
PMid:26098131 PMCid:PMC4996078
[9]. Kulikowski CA, Shortliffe EH, Currie LM, Elkin PL, Hunter LE, Johnson TR, et al. AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline. J Am Med Inform Assoc. 2012;19(6):931-8.
https:\/\/doi.org\/10.1136\/amiajnl-2012-001053
PMid:22683918 PMCid:PMC3534470
[10]. Prewitt JMS, Mendelsohn ML. The Analysis Of Cell Images. Annals of the New York Academy of Sciences. 2006 ;128(3):1035-53.
https:\/\/doi.org\/10.1111\/j.1749-6632.1965.tb11715.x
PMid:5220765
[11]. Zhang Y, Ouyang Z, Zhao H. A statistical framework for data integration through graphical models with application to cancer genomics. The Annals of Applied Statistics. 2017 Mar 1;11(1).
https:\/\/doi.org\/10.1214\/16-AOAS998
[12]. Li W, Li J, Sarma KV, Ho KC, Shen S, Knudsen BS, et al. Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging. 2019 Apr;38(4):945-54.14.
https:\/\/doi.org\/10.1109\/TMI.2018.2875868
PMid:30334752 PMCid:PMC6497079
[13]. LeCun Y, Bagnio Y, Hinton G. Deep Learning. Nature. 2015 May;521(7553):436-44.
https:\/\/doi.org\/10.1038\/nature14539
PMid:26017442
[14]. Syrykh C, Abreu A, Amara N, Siegfried A, Maisongrosse V, Frenois FX, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. npj Digital Medicine. 2020 May 1;3(1).
https:\/\/doi.org\/10.1038\/s41746-020-0272-0
PMid:32377574 PMCid:PMC7195401
[15]. Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, et al. Deep learning-enabled medical computer vision. npj Digit Med. 2021 Jan 8;4(1):1-9.
https:\/\/doi.org\/10.1038\/s41746-020-00376-2
PMid:33420381 PMCid:PMC7794558
[16]. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Feny\u00f6 D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018 Oct;24(10):1559-67.
https:\/\/doi.org\/10.1038\/s41591-018-0177-5
PMid:30224757 PMCid:PMC9847512
[17]. Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, et al. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer. 2022 Sep;10(9):e005292.
https:\/\/doi.org\/10.1136\/jitc-2022-005292
PMid:36180071 PMCid:PMC9528623
[18]. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine [Internet]. 2019 Aug 1;25(8):1301-9.
https:\/\/doi.org\/10.1038\/s41591-019-0508-1
PMid:31308507 PMCid:PMC7418463
[19]. Madani A, Ong JR, Tibrewal A, Mofrad MRK. Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit Med. 2018;1:59.
https:\/\/doi.org\/10.1038\/s41746-018-0065-x
PMid:31304338 PMCid:PMC6550282<\/p>\n

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\n \t Cite this article:<\/strong><\/span> M. Haripriya, Sharad Gavhane, K.Vijayakumar. Automated Classification of Normal Histological Tissues Using Convolution neural network: A ResNet-50-Based Educational Tool. Int J Anat Res 2025;13(3):9306-9313. DOI:\u00a0<\/strong>10.16965\/ijar.2025.201\u00a0 \n <\/div>\t\n\t\t\n\n\n

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Type of Article: \u00a0Original Research Volume 13; Issue 3 (September 2025) Page No.: 9306-9313 DOI:\u00a0https:\/\/dx.doi.org\/10.16965\/ijar.2025.201 Automated Classification of Normal Histological Tissues Using Convolution neural network: A ResNet-50-Based Educational Tool Haripriya 1, Sharad Gavhane 2, K.Vijayakumar *3. 1 Associate Professor, Department of Anatomy, Sri Ramachandra Medical College & Research Institute (SRMC & RI), Sri Ramachandra Institute
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