Abstract
Breast cancer is one of the primary causes of cancer death in the world and has a great impact on women’s health. Generally, the majority of classification methods rely on the high-level feature. However, different levels of features may not be positively correlated for the final results of classification. Inspired by the recent widespread use of deep learning, this study proposes a novel method for classifying benign cancer and malignant breast cancer based on deep features. First, we design Sliding + Random and Sliding + Class Balance Random window slicing strategies for data preprocessing. The two strategies enhance the generalization of model and improve classification performance on minority classes. Second, feature extraction is based on the AlexNet model. We also discuss the influence of intermediate- and high-level features on classification results. Third, different levels of features are input into different machine-learning models for classification, and then, the best combination is chosen. The experimental results show that the data preprocessing of the Sliding + Class Balance Random window slicing strategy produces decent effectiveness on the BreaKHis dataset. The classification accuracy ranges from 83.57% to 88.69% at different magnifications. On this basis, combining intermediate- and high-level features with SVM has the best classification effect. The classification accuracy ranges from 85.30% to 88.76% at different magnifications. Compared with the latest results of F. A. Spanhol’s team who provide BreaKHis data, the presented method shows better classification performance on image-level accuracy. We believe that the proposed method has promising good practical value and research significance.
Highlights
In recent years, the global prevalence of breast cancer (BC) has been gradually increased, and the affected organisms tend to be younger, gender-neutral, and have racial ambiguity, which has posed a huge threat to human beings’ normal life
The trained network model extracts features by choosing node. en, the extracted features are input into the ML model to classify. e experiment is performed using a fivefold cross-validation approach, the same folds released with the BreaKHis dataset. e experimental results show that the average classification accuracy and standard deviation of pathological tissue images at different magnifications (40×, 100×, 200×, and 400×) are 87.85%, 86.68%, 87.75%, and 85.30%, respectively. e average classification accuracy and standard deviation of patients at different magnifications (40×, 100×, 200×, and 400×) are 87.93%, 87.41%, 88.76%, and 85.55%, respectively
We try four different window slicing strategies, including Sliding window slicing, Random window slicing, Sliding + Random window slicing, and Sliding + Class Balance Random window slicing. e experimental results show that Sliding window slicing strategy can guarantee model’s fitting training, but cannot guarantee generalization training
Summary
The global prevalence of breast cancer (BC) has been gradually increased, and the affected organisms tend to be younger, gender-neutral, and have racial ambiguity, which has posed a huge threat to human beings’ normal life. In 2018, the World Health Organization’s International Agency estimated that there were 2.1 million new female cases of BC among women about 25 percent of all cancers. E number of female cases was far greater than any other cancers in both developed and developing countries [1]. Diagnosis and treatment can effectively reduce the risk of diseases and prevent the progression of cancers [2]. Pathologists need to combine the feedback from medical equipment with their own diagnostic experience to test and analyze sample information for cancer diagnosis and treatment strategy. Due to the uneven distribution of pathologists and medical resources around the world, it is difficult to ensure timely and effective treatment for patients in remote areas and underdeveloped countries [4]. Due to the uneven distribution of pathologists and medical resources around the world, it is difficult to ensure timely and effective treatment for patients in remote areas and underdeveloped countries [4]. erefore, an efficient, low-cost, and objective diagnosis method has important social significance and research value
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