Abstract
Cervical cancer is one of the most common cancers among women in the world, over 570,000 patients are affected annually. Pathological examination for patients using Pap Smear becomes the mainstream of cervical cancer diagnoses. Accurate diagnoses and analyses largely rely on 3 factors: cell segmentation, feature extraction and selection as well as classification. Firstly, a 2-layer segmentation algorithm based on block Maximum Between-Class Variance (Otsu) and Gradient Vector Flow (GVF) Snake model is applied to obtain regions of interest (ROI). Then the features of chroma, shape and texture are extracted and selected for a better classification performance. The random forest algorithm based on Artificial Fish Swarms Algorithm (AFSA) is used to recognize and classify cervical epithelial cells. The proposed methods were tested on 200 cervical Pap Smear images. Experimental results show that cervical cells can be segmented with an effective segmentation result. The proposed feature selection method achieved an accuracy of 81.31% with the minimum feature number. The improved random forest algorithm with 2 and 7 classification under fivefold cross-validation reaches the highest classification accuracy (96.86%). Experimental results showed that the proposed method has obvious recognition advantages, and thus provides a practical classification frame for the diagnoses of cervical cancer.
Highlights
As we all know, the incidence rate of cervical cancer in gynecological tumors is just lower than breast cancer, which is exactly a female killer
In order to deal with the challenges in both biology and computation that decreased the effect of recognition, we proposed an improved random forest algorithm for cervical epithelial cell detection in this paper, which contains 3 major parts: (1) Cell segmentation: block Otsu algorithm for the first segmentation and Gradient Vector Flow (GVF) Snake model for the second segmentation
The classifier adopts the random forest classifier based on Artificial Fish Swarms Algorithm (AFSA) proposed in this paper, feature selection methods applied for the experiment are as shown in Table 10, epithelial cells were used as the classification data
Summary
The incidence rate of cervical cancer in gynecological tumors is just lower than breast cancer, which is exactly a female killer. As for deep learning methods, comparison-based CNN for cervical cells detection by Liang et al [40] and deep belief network verified by Rasche et al [41] achieved higher accuracy when compared to a traditional methodology in segmentation and feature extraction.
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