Transfer Learning with Feature Extraction Modules for Improved Classifier Performance on Medical Image Data

Transfer learning attempts to use the knowledge learned from one task and apply it to improve the learning of a separate but similar task. This article proposes to evaluate this technique's effectiveness in classifying images from the medical domain. The article presents a model TrFEMNet (Transfer Learning with Feature Extraction Modules Network), for classifying medical images. Feature representations from General Feature Extraction Module (GFEM) and Specific Feature Extraction Module (SFEM) are input to a projection head and the classification module to learn the target data. The aim is to extract representations at different levels of hierarchy and use them for the final representation learning. To compare with TrFEMNet, we have trained three other models with transfer learning. Experiments on the COVID-19 dataset, brain MRI binary classification, and brain MRI multiclass data show that TrFEMNet performs comparably to the other models. Pretrained model ResNet50 trained on a large image dataset, the ImageNet, is used as the base model.

Automated summary

Transfer learning is a technique that utilizes knowledge learned from one task to improve learning for another similar task. This article proposes TrFEMNet, a model for classifying medical images, which extracts representations at different levels of hierarchy for improved performance. Experimental results show that TrFEMNet performs comparably to other models in various medical image classification tasks.