Breast cancer has become the most common cancer in the world, and biopsy is the most reliable and widely used technique for detecting breast cancer. However, observation of histopathological images is time-consuming and labor-intensive. Currently, CNN has become the mainstream method for breast cancer histopathological image classification research. However, some studies have found that the optical microscope-generated histopathological images have noise, and the output of a well-trained convolutional neural network in image classification tasks can change drastically due to small variations in the input. Therefore, the quality of the image significantly affects the accuracy of the classification. Wavelet transform is a commonly used denoising method, but the selection of the threshold is a difficult problem, and traditional methods are difficult to find the appropriate threshold quickly and accurately. This paper proposes an adaptive threshold selection method that combines threshold selection steps with deep learning methods by using the threshold as a parameter in the CNN model to train. In this way, we associate the threshold with the classification result of the model and find the appropriate value for that image and task by back-propagation in training. The method was experimented on publicly available datasets BreaKHis and BACH. The results in BreaKHis (40x: 94.37 % , 100x: 93.85 % , 200x: 91.63 % , 400x: 93.31 % ), and BACH (91.25 % ) demonstrate that our adaptive threshold selection method can improve classification accuracy and is significantly superior to traditional threshold selection methods.

Yujian Liu

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