Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN), breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs.
Ünaldı et al. (Sun,) studied this question.