Automating Papanicolaou Test Using Deep Convolutional Activation Feature
| Autor: | Jonghwan Hyeon, Kap No Lee, Ho-Jin Choi, Byung Doo Lee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Cervical cancer
Computer science business.industry Feature extraction 02 engineering and technology Papanicolaou Test 010501 environmental sciences Machine learning computer.software_genre medicine.disease 01 natural sciences Convolutional neural network medicine.anatomical_structure Feature (computer vision) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence F1 score business Cervix computer 0105 earth and related environmental sciences Test data |
| Zdroj: | MDM |
| DOI: | 10.1109/mdm.2017.66 |
| Popis: | Cervical cancer is the women's fourth most common cancer worldwide, with 266,000 deaths in a year. Cervical cancer can be diagnosed by the Papanicolaou test. In this test, a cytopathologist observes a microscopic image of the cervix cells and decides whether the patient is abnormal or not. According to research, the accuracy of the cervical cytology is reported as 89.7%. Because it is associated with the patient's life, it is important to improve the accuracy of this test. Many systems have been proposed to help judge experts to improve the accuracy of tests in the medical field, but development has been limited to areas where there are cleanly quantified test data. In this paper, we design and train a model to automatically classify the normal/abnormal state of cervical cells from microscopic images by using a convolutional neural network and several machine learning classifiers. As a result, the support vector machine achieves the highest performance with 78% F1 score. |
| Databáze: | OpenAIRE |
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