A Lightweight Conformer-Based Framework for Medical Image Classification
A pretrained lightweight Conformer model tailored for medical image classification that integrates convolutional layers for capturing fine-grained spatial features with transformer blocks that capture long-range dependencies, creating a unified architecture capable of robust representation learning.