Leveraging Pre-trained Models: A Comparative Analysis of Transfer Learning Strategies for medical image analysis
Yaddu Sai Yuvaraj, Neha Dhariwal · 2024
This role is critical for the early diagnosis and detection of diseases, among them cancer, through the analysis of medical images. In this paper, different transfer-learning approaches are compared regarding the task of breast cancer detection using deep pretrained Convolutional Neural Networks. Custom model CNN and transfer learning using VGG16 architecture were implemented. Such a dataset consists of images of breast cancers labeled as healthy, 0, and cancerous, 1. Finally, all this work integrates a performance metric evaluation that enables Gradio for the prediction of the real-time interactive user interface. Our results show that this fine-tuning significantly increased accuracy with less training time compared to custom models; hence, it may be useful for extensive medical image classification tasks.