Evaluating the Deep Learning Models Performance for Segmentation of Oral Epithelial Dysplasia: A Histological Data-Driven Approach

Abstract

Oral epithelial dysplasia (OED) poses a significant precancerous risk, potentially progressing to oral squamous cell carcinoma (OSCC). Precise segmentation of OED within histopathological images is pivotal for early diagnosis and treatment planning. This study evaluates Deep Learning (DL) models for precise Oral Epithelial Dysplasia (OED) segmentation in biopsy slide images. The Vanilla UNET model is explored with the standard UNET and other transfer learning models (VGG16, VGG19, MobileNet, and DeepLabV3+) as the backbone of the model. For our application, U-Net demonstrated superior performance (IoU: 93.73%, precision: 97.96%, recall: 97.78%, F1-score: 96.76%). Visual examples highlight model strengths and limitations, providing insights beyond traditional metrics. This research advances computer-aided histopathological analysis, emphasizing DL models’ crucial role in enhancing diagnostic accuracy and patient care.