Deep Transfer Learning for Breast Cancer Classification
This work addresses breast cancer diagnosis for medical applications, but it is incremental as it applies existing transfer learning methods to a known dataset without introducing new techniques.
The study tackled breast cancer classification by comparing deep transfer learning models like VGG, Vision Transformers, and ResNet on Invasive Ductal Carcinoma images, achieving a top accuracy of 90.40% with ResNet-34 and noting VGG-16's higher F1-score due to fewer parameters.
Breast cancer is a major global health issue that affects millions of women worldwide. Classification of breast cancer as early and accurately as possible is crucial for effective treatment and enhanced patient outcomes. Deep transfer learning has emerged as a promising technique for improving breast cancer classification by utilizing pre-trained models and transferring knowledge across related tasks. In this study, we examine the use of a VGG, Vision Transformers (ViT) and Resnet to classify images for Invasive Ductal Carcinoma (IDC) cancer and make a comparative analysis of the algorithms. The result shows a great advantage of Resnet-34 with an accuracy of $90.40\%$ in classifying cancer images. However, the pretrained VGG-16 demonstrates a higher F1-score because there is less parameters to update. We believe that the field of breast cancer diagnosis stands to benefit greatly from the use of deep transfer learning. Transfer learning may assist to increase the accuracy and accessibility of breast cancer screening by allowing deep learning models to be trained with little data.