Automated Classification of Normal Histological Tissues Using Convolution neural network: A ResNet-50-Based Educational Tool

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.