Comparison between ConvNeXt and ViT models for classification of gastrointestinal diseases
This project utilizes images from the KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection. The primary goal is to classify gastrointestinal images into multiple disease categories to support automated diagnosis and medical analysis.
As part of this work, we compare the performance of two state-of-the-art deep learning architectures: ConvNeXt and Vision Transformers (ViT), evaluating their effectiveness in medical image classification tasks.