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Design and Evaluation of Cervical Pap Smear E-learning System for the Education of Cytopathology |
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PP: 617-626 |
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Author(s) |
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Po-Chi Huang,
Jen-Yung Lin,
Chung-Chuan Cheng,
Yung-Fa Huang,
Shou-Wei Chien,
Yung-Fu Chen,
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Abstract |
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| Cytology evaluation is a safe, efficient, and well-established technique for the diagnoses of many diseases. Its ability to
reduce the mortality and morbidity of cervical cancer is through mass screening to early detect dysplasia or pre-invasive cancer cells.
Classical cytological diagnosis is based on microscopic observation of specialized cells and qualitative assessment using descriptive
criteria, which may be inconsistent because of subjective variability of different observers. Recently, web-based learning is becoming
prevalent in schools and enterprises around the world for its advantages of providing easy access to information and knowledge,
supporting ubiquitous learning environment, and increasing cost-effectiveness for both educational institution and students. The
objectives of this study were to design automatic classifiers based on integrated genetic algorithm (GA) and support vector machine
(SVM) to cluster four different types of cervical cells and to discriminate dysplasia from normal cells, as well as to implement
a web-based cytopathology training and testing system to increase learning efficiency of cytopathologic education. A prototypic
system composed of a microscope, digital camera, personal computer, cellular processing and analyzing program, and cell classifier
was designed to facilitate acquisition, image processing and analysis, and classification of cell images. Furthermore, a web-based
cytopathology training and testing (WBCTT) systems were developed based on the classified cell images to train students, resident
physicians, and novice pathologists to discriminate various types of cervical cells. The experimental results demonstrate that the
classification and diagnostic accuracy achieves 96.82% and 99.6% , respectively. System evaluation based on questionnaire survey
of extended technology acceptance model (TAM) shows that the proposed system embedded with cell classifier and WBCTT is useful
in cytopathology diagnosis and training.Most of the users agreed the operation interface is friendly and easy to use. They also expressed
strong behaviour intention to further adopt the system. It is expected to have significant contributions in increasing diagnostic efficiency
and promoting learning efficiency. |
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