Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images

Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells...

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Bibliographic Details
Main Authors: Badarneh, Alaa, Alzuet, Ala'a, Mustafa, Wan Azani, Alquran, Hiam, Alsalatie, Mohammed, Mohammed, F. F., Alkhayyat, Ahmed
Format: Conference Proceeding
Language:English
Subjects:
Accuracy evaluation
Artificial intelligence
Automated cell counting
Cell nuclei
Cervical cancer
Data models
Extended minima transformation
Image enhancement
Image techniques
Manual counting
Manuals
Micrograph
Morphological characteristics
Pap smear test
Photomicrography
Reliability
Small pixels removal
Training
Transforms
Whole slide images
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Summary:Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells in whole slide images automatically by employing various image techniques. The proposed approach consists of four main steps; image enhancement, transform the extended minima, remove small pixels, and count the number of abnormal cells in the image. The proposed system used 250 cervical pap smear images where the overlap between cells is minimal. The performance of the proposed system is evaluated based on comparing the manual counting and automating counting over whole images. Therefore, the accuracy is evaluated mainly on the difference between manual and automated, and it is 92.5%. The proposed method can be used in laboratory to decrease the false positive rates in counting abnormal cells.
ISSN:2831-753X
DOI:10.1109/IICETA57613.2023.10351426