Impact of Transfer Learning on Convolutional Neural Networks for Odontogenic Tumor Diagnosis.

ABSTRACT:

Objective

This study aimed to evaluate the coherence between data heterogeneity and model complexity by comparing seven convolutional neural network (CNN) architectures-trained with and without ImageNet pretraining-in a multiclass framework for the histopathological classification of three odontogenic tumors: adenomatoid odontogenic tumor, ameloblastoma, and ameloblastic carcinoma. The goal was to investigate how transfer learning influences performance and diagnostic reliability in a clinically relevant context characterized by overlapping histological patterns.

Methods

An international, multicenter cross-sectional dataset of 64 hematoxylin- and eosin-stained whole slide images was analyzed, including adenomatoid odontogenic tumor (n = 16), ameloblastoma (n = 27), and amelo

SUBMITTER: Giraldo-Roldan D 

PROVIDER: S-EPMC12920825 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature