IJAR.2025.201
Type of Article: Original Research
Volume 13; Issue 3 (September 2025)
Page No.: 9306-9313
DOI: https://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 for Higher Education and Research (SRIHER), Chennai, Tamil Nadu, India. ORCiD: https://orcid.org/0000-0001-6136-7576
2 Medical officer Department of General Medicine, Symbiosis Medical College and hospital (SUHRC), Symbiosis International (Deemed) University (SIU) Pune, Maharashtra, India. ORCiD: https://orcid.org/0009-0001-6627-4560
*3 Assistant Professor, Department of Anatomy, Symbiosis Medical College for Women & Symbiosis University Hospital and Research Centre, Symbiosis International (Deemed University), Pune, India. ORCiD: https://orcid.org/0000-0003-3032-8974
Corresponding Author: Dr. K. 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: kvijay.india@gmail.com
ABSTRACT
Background: 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.
Aim: 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.
Methods: 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, EfficientNet-B0, and MobileNetV2.
Results: 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.
Conclusion: 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.
Keywords: Histology, Deep Learning, ResNet-50, Convolutional Neural Networks, Digital Pathology, Medical Education.
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