Classification of Lesions in Capsule Endoscopy Images using Swin Transformer and Semi-Supervised Learning - Oliveira, Alejandro Costa de; Cella, Mario Vítor Vieira; Quintanilha, Darlan Bruno Pontes; Soares Filho, Celso Luiz Silva; Clímaco, Francisco Glaubos Nunes; Borchartt, Tiago Bonini; Paiva, Anselmo Cardoso de (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Automated analysis of images obtained by Wireless Capsule Endoscopy (WCE) is a significant challenge in the medical field, especially due to the difficulty in lesion detection, the scarcity of labeled samples, and the high visual variability of the images. This work proposes a multiclass classification method for luminal findings in WCE images based on the Swin Transformer architecture, structured in two sequential stages: a binary classifier, responsible for filtering normal from anomalous images, followed by a multiclass classifier for identifying the specific lesion. To address the limitation of labeled data, offline data augmentation and semi-supervised learning techniques were employed. Experiments performed on the Kvasir-Capsule array with six classes of luminal findings demonstrated that Transformer-based architectures consistently outperform traditional CNN models such as ResNet-50, MobileNetV3, and EfficientNetV2. The Swin Transformer model achieved 98% accuracy and an F1-score in the binary step and 86% in multiclass classification, representing a gain of 4 percentage points compared to purely supervised training.