Deep Convolutional Neural Network for Accurate Classification of Myofibroblastic Lesions on Patch-Based Images

Author(s)

Giraldo-Roldan, Daniela ; dos Santos, Giovanna Calabrese ; Araujo, Anna Luiza Damaceno ; Nakamura, Thais Cerqueira Reis ; Pulido-Diaz, Katya ; Lopes, Marcio Ajudarte ; Santos-Silva, Alan Roger ; Kowalski, Luiz Paulo ; Moraes, Matheus Cardoso ; Vargas, Pablo Agustin

• Document type: Journal article
• Source: Head and Neck Pathology; v. 18, n. 1, p. 9-pg., 2024-10-28.

Abstract

ObjectiveThis study aimed to implement and evaluate a Deep Convolutional Neural Network for classifying myofibroblastic lesions into benign and malignant categories based on patch-based images.MethodsA Residual Neural Network (ResNet50) model, pre-trained with weights from ImageNet, was fine-tuned to classify a cohort of 20 patients (11 benign and 9 malignant cases). Following annotation of tumor regions, the whole-slide images (WSIs) were fragmented into smaller patches (224 x 224 pixels). These patches were non-randomly divided into training (308,843 patches), validation (43,268 patches), and test (42,061 patches) subsets, maintaining a 78:11:11 ratio. The CNN training was caried out for 75 epochs utilizing a batch size of 4, the Adam optimizer, and a learning rate of 0.00001.ResultsResNet50 achieved an accuracy of 98.97%, precision of 99.91%, sensitivity of 97.98%, specificity of 99.91%, F1 score of 98.94%, and AUC of 0.99.ConclusionsThe ResNet50 model developed exhibited high accuracy during training and robust generalization capabilities in unseen data, indicating nearly flawless performance in distinguishing between benign and malignant myofibroblastic tumors, despite the small sample size. The excellent performance of the AI model in separating such histologically similar classes could be attributed to its ability to identify hidden discriminative features, as well as to use a wide range of features and benefit from proper data preprocessing. (AU)

• FAPESP's process: 09/53839-2 - Creation of a Digital Pathology Laboratory using a histological slidescanner
• Grantee: Oslei Paes de Almeida
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 21/14585-7 - Artificial intelligence applied to the clinical and histopathological diagnosis of Head and Neck Cancer
• Grantee: Anna Luiza Damaceno Araujo
• Support Opportunities: Scholarships in Brazil - Post-Doctoral