Blending Ensemble of Fine-Tuned Convolutional Neural Networks Applied to Mammary Image Classification

Abstract

Medical images classification is a challenging research topic in the field of computer vision, especially when applied to diagnosis of breast cancer (BC). Nowadays, histopathological image is marked as the gold standard for diagnosing BC. However, such diagnosis is heavily dependent on the clinician's experience, which is extremely time consuming and is subjected to human error even for experienced doctors. To address those problems, this paper implements an automated method for distinguishing the benign from the malignant tumor based on a convolutional neural network (CNN). Traditional deep CNN and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between some key features and image tags. To further meet the high accuracy requirement of diagnosis, according to the characteristics of histopathological images, we propose a novel CNN framework. Firstly, a normal image is augmented to solve the problem about having a limited database. Secondly, we introduce transfer learning to obtain more accurate weight parameters that were pre-trained on the ImageNet. Thirdly, we combine various features extracted by many individual models to obtain comprehensive features. Finally, random forest is introduced to enforce classification. The experimental results show that novel CNN frameworks have better performance compared with individual models, including DenseNet and ResNet. Experimental results are able to prove the effectiveness of our strategy.