A State-of-the-art Deep Transfer Learning-Based Model for Accurate Breast Cancer Recognition in Histology Images

Abstract

Breast cancer is widely prevalent in women. To diagnose it, pathologists evaluate breast histopathology images under a microscope in various magnifications. However, the number of pathologists per population is low in many countries, and a mistake is probable. Nowadays, deep learning has become a popular artificial intelligence research trends. Even though deep neural networks have obtained promising results in image processing, the need for extensive training data avoids its use in medical image processing. After the evaluation of various state-of-the-art deep learning methods and algorithms in medical data processing, this study proposes an effective deep transfer learning-based model, relying on pre-trained DCNN using an extensive data of ImageNet dataset which improves state-of the-art systems in both binary and multi-class classification. The weights of the pre-trained DesneNet121 on the Imagenet are transferred as initial weights first, and then the model is fine-tuned with a deep classifier along with data augmentation to distinguish various malignant and benign samples in the two categories of binary and multi-class classification. In the multi-class classification, the proposed model obtained up to 97% image-level accuracy. In binary classification, the model obtained up to 100% image-level accuracy. The achieved-results are outperforming previous studies accuracies in multiple performance metrics in breast cancer CAD systems. Moreover, the proposed method is both flexible and scalable, meaning it can be easily expanded to cover the detection of other types of diseases in the future and be integrated with more CNNs to increase its generalization capabilities.