Abstract
The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class variances in breast cancer histopathological images, extracting features for breast cancer classification is difficult. To address this problem, an improved autoencoder (AE) network using a Siamese framework that can learn the effective features from histopathological images for CAD breast cancer classification tasks was designed. First, the inputted image is processed at multiple scales using a Gaussian pyramid to obtain multi-scale features. Second, in the feature extraction stage, a Siamese framework is used to constrain the pre-trained AE so that the extracted features have smaller intra-class variance and larger inter-class variance. Experimental results show that the proposed method classification accuracy was as high as 97.8% on the BreakHis dataset. Compared with commonly used algorithms in breast cancer histopathological classification, this method has superior, faster performance.
Highlights
IntroductionIn 2020, the Global Center for Cancer Research released a statistical report on the incidence and mortality of 36 types of cancers in 185 countries
We conducted a series of experiments on a common breast cancer histopathology dataset to evaluate the performance of the improved AE model
Support Vector Machine (SVM), Parameter-Free Threshold Adjacency Statistic (PFTAS) + Random Forest (RF), Inception_v3, Resnet50, Inception_resnet_v2, and Xception were used as control models
Summary
In 2020, the Global Center for Cancer Research released a statistical report on the incidence and mortality of 36 types of cancers in 185 countries. The report states that breast cancer is the most commonly diagnosed cancer, accounting for 11.7% of total cancer cases [1]. Detection of breast cancer is paramount in reducing the mortality rate. With the development of computer and artificial intelligence technology, CADs can help doctors diagnose breast cancer while improving diagnostic efficiency and accuracy [2,3,4]. Medical imaging is the most effective method of primary screening for breast cancer. The above methods determine whether a breast is cancerous by analyzing changes in the shape of the breast. The prognostic model automatically extracts useful information from the images, without the need for specialist judgement [7]
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