Segmentation and Cervical Cancer Classification

Cervical cancer does not occur as often as brain and lung cancer, however it still possesses an incredible threat to women every year. While it can be cured if found in an earlier stage, many women die every year due to the fact that their cancer was not detected at an earlier time and symptoms did not appear until the cancer was too far advanced to treat. Cells in the cervix can either by squamous cells, which when infected cause squamous cell carcinoma, or glandular cells which cause adenocarcinoma. Squamous cell carcinomas are flat cells that vary in morphology and cover the surface of the cervix. Adenocarcinoma cells develop in the glandular cells which produce music and grow along the inside of the endocervix, the passage that runs from the cervix to the womb. While both squamous cells carcinomas and adenocarcinomas are both associated with HPV infection, adenocarcinomas are much less common (Conceição, Braga, Rosado, & Vasconcelos, 2019, p. 2). Because cervical cancer is difficult to detect, and hard to treat if it has progressed too far, automated machines that can detect cervical cancer would significantly improve survival rate of women suffering from the disease.

Segmentation is a type of study and imaging technique that can be used to identify certain spots in an image that may have cancerous lesions. Segmentation is the technique of dividing an image into separate parts and is used for identifying objects within images when the whole image cannot be processed. Segmentation is the most important step of cytology or pap smears as it identifies cells based on their structures and morphology. 


A study performed in 2018 proposed a machine assisted by artificial intelligence which could detect cervical cancer in patients. The first step was to take a cervical cancer image and enhance the contrast of the image for better visibility using Oriented Local Histogram Equalization. Certain features such as roundness, sides, and circularity were then extracted from the image and used to train the neural network classifier. The features were extracted in order to discriminate between a healthy cervical image and a cancerous one. The neural network classifier identified the cervical image as either benign or malignant by comparing it to the features used for training. To classify the tumor, a feed forward backpropagation neural network was used to reach the highest possible accuracy. This type of neural network is built using three layers made up of neurons. The input layer accepts the elements of the features that were extracted. Three hidden layers between the input and output layer are also used, each of which have a different number of neurons.  Each layer is connected with all of the neurons in the previous layer. The output layer outputs the response of the neural network. The response is created as either zero or one; the lower value means the image was classified as normal, and a higher value means it was categorized as cancerous. After classification, the model was then studied to find the average sensitivity, specificity and accuracy. All three of these values were over 95% (P & M, 2018, p. 1). 


An automated machine such as this one that can detect and classify abnormalities in cervical images by extracting features and analyzing them would greatly improve the medical field. AI models like this can be used in the future to even further classify images of cancer cells as early or advanced to help physicians determine the proper course of treatment. However, although AI assisted imaging for cancer detection has many promises, new technologies do come with new perils, some of which will be covered in my next blog post.

References

P, E., & M, S. (2018, December 25). Automatic Approach for Cervical Cancer Detection and Segmentation Using Neural Network Classifier. Retrieved October 02, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428557/

Conceição, T., Braga, C., Rosado, L., & Vasconcelos, M. (2019, October 15). A Review of Computational Methods
            for Cervical Cells Segmentation and Abnormality Classification. Retrieved October 02, 2020, from           https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834130/

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