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. 2022 May 30;9(6):240.
doi: 10.3390/bioengineering9060240.

Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images

Vitalii Pavlov  1   2 , Stanislav Fyodorov  1 , Sergey Zavjalov  1 , Tatiana Pervunina  2 , Igor Govorov  2 , Eduard Komlichenko  2 , Viktor Deynega  2 , Veronika Artemenko  2
Affiliations

Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images

Vitalii Pavlov et al. Bioengineering (Basel). .

Abstract

The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions of the cervix. Cervical cancer (CC) is one of the most common cancers in women worldwide, especially in middle- and low-income countries. Therefore, there is a growing demand for approaches that aim to detect precancerous lesions, ideally without quality loss. Despite its high efficiency, this method has some disadvantages, including subjectivity and pronounced dependence on the operator's experience. The objective of the current work is to propose an alternative to overcoming these limitations by utilizing the neural network approach. The classifier is trained to recognize and classify lesions. The classifier has a high recognition accuracy and a low computational complexity. The classification accuracies for the classes normal, LSIL, HSIL, and suspicious for invasion were 95.46%, 79.78%, 94.16%, and 97.09%, respectively. We argue that the proposed architecture is simpler than those discussed in other articles due to the use of the global averaging level of the pool. Therefore, the classifier can be implemented on low-power computing platforms at a reasonable cost.

Keywords: cervical cancer; colposcopy; convolutional neural networks; pathologies; suspicious for invasion.

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Conflict of interest statement

The authors declare no conflict of interest. The funders/sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Example colposcopic images of normal cervices and various degrees of abnormal lesions: normal (a), LSIL (b), HSIL (c), and suspicious for invasion (d).
Figure 2
Figure 2
Examples of prepocessing (grayscale) of colposcopic images: normal (a) and suspicious for invasion (b).
Figure 3
Figure 3
Examples of prepocessing (elimination of glares) colposcopic images: normal (a) and suspicious for invasion (b).
Figure 4
Figure 4
CNN Classifier architecture.
Figure 5
Figure 5
The confusion matrix for the test dataset.
Figure 6
Figure 6
Examples of errors in decisions (predicted/real): (a) suspicious for invasion/normal, (b) suspicious for invasion/HSIL, (c) suspicious for invasion/HSIL, and (d) LSIL/HSIL.
Figure 7
Figure 7
Illustration of the application of the developed approach.
See this image and copyright information in PMC

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