A HYBRID deep learning model improved gastrointestinal image classification by addressing visual similarity between classes and imbalanced data, achieving a macro F1 score of 0.718 and a micro F1 score of 0.897 in testing across 22 classes.
Researchers investigated a framework combining two established neural network architectures with a Coordinate Attention mechanism and focal loss. The approach aimed to improve the representation of distinguishing features in gastrointestinal endoscopic images, potentially supporting more accurate classification of gastrointestinal diseases.
Addressing Challenges in Gastrointestinal Image Classification
Deep learning has shown promise in endoscopic image analysis, but classification performance can be undermined by imbalanced class distributions and visual similarities between different categories. These challenges can make it more difficult for models to distinguish between image classes reliably.
To address these limitations, the researchers developed a hybrid framework combining ResNet50 and VGG16. Each architecture was individually optimised using Coordinate Attention before their extracted features were concatenated into a single hybrid model.
This feature level integration was designed to strengthen image representation by combining information from both architectures. The resulting framework was further refined using focal loss to address class imbalance and improve the model’s handling of difficult and underrepresented samples.