Abstract
To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms (P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant (P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.
Highlights
Breast cancer is the highest incidence rate of cancer in women
The cell detection results of pathological images based on the optimized convolutional neural network (CNN) algorithm in this study were compared with those of iterative radial voting (IRV) [23] and maximum stable extremum region (MSER) [24] (Figures 7–9)
A local region active contour (LRAC) model initialized based on deep learning CNN was used for cell modeling, detection, and segmentation. e model included two aspects: (1) accurate cell detection and localization based on deep learning convolutional neural network and (2) automatic cell segmentation based on LRAC model [25]
Summary
Breast cancer is the highest incidence rate of cancer in women. Breast tissue histopathologic images are highly accurate and reliable. ey are commonly used in the diagnosis and classification of breast cancer. ere is a certain correlation between the pathological grading of breast cancer and the morphology and topological structure of breast cancer. Histology is a science to study the microscopic results of animals and plants It is a key step in modern diagnostic medicine and a powerful tool to study the pathogenesis and biological treatment processes (such as cancer and embryogenesis) [1]. Pathologists can observe and analyze by computer, not just face-to-face microscopic guess tissue slice analysis [2]. E purpose of automatic pathological image analysis is to quickly find the lesion area or resected tumor tissue pathological grade in hundreds of whole scan images (WSIS) by using machine learning and image processing methods on the basis of digital pathological images [4] and automatically give a pathological grade and diagnostic information from visual pathological images [5]. Doctors’ subjective evaluation has the influence of emotion, fatigue, and disease slicing proficiency [8], resulting in differences in classification results, which is not Journal of Healthcare Engineering conducive to the formulation of the clinical treatment plan [9]. e computer-aided diagnosis system adopts a machine learning algorithm to develop an automatic quantitative analysis system [10]
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