Convolutional neural network classification of cancer cytopathology
  images: taking breast cancer as an example
MingXuan Xiao Yufeng Li Xu Yan Min Gao Weimin Wang
Breast cancer is a relatively common cancer among gynecological cancers. Its
diagnosis often relies on the pathology of cells in the lesion. The
pathological diagnosis of breast cancer not only requires professionals and
time, but also sometimes involves subjective judgment. To address the
challenges of dependence on pathologists expertise and the time-consuming
nature of achieving accurate breast pathological image classification, this
paper introduces an approach utilizing convolutional neural networks (CNNs) for
the rapid categorization of pathological images, aiming to enhance the
efficiency of breast pathological image detection. And the approach enables the
rapid and automatic classification of pathological images into benign and
malignant groups. The methodology involves utilizing a convolutional neural
network (CNN) model leveraging the Inceptionv3 architecture and transfer
learning algorithm for extracting features from pathological images. Utilizing
a neural network with fully connected layers and employing the SoftMax function
for image classification. Additionally, the concept of image partitioning is
introduced to handle high-resolution images. To achieve the ultimate
classification outcome, the classification probabilities of each image block
are aggregated using three algorithms: summation, product, and maximum.
Experimental validation was conducted on the BreaKHis public dataset, resulting
in accuracy rates surpassing 0.92 across all four magnification coefficients
(40X, 100X, 200X, and 400X). It demonstrates that the proposed method
effectively enhances the accuracy in classifying pathological images of breast
cancer.