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Stages Classification on Endometrial Cell Images Using Roundness Shape Analysis

Uterine cancer, also known as endometrial cancer, is a form of cancer that affects the female reproductive system. Women over the age of 50 are more likely to get uterine cancer. At this current time, there are two methods that the physician or health care provider tends to use to diagnose cancer, which are ultrasound technique and endometrial biopsy. If the patient is suspected of having cancer, ultrasound is first used to measure the length of the endometrial lining. Then, if the lining is greater than 4mm, an endometrial biopsy is performed. At this stage, small tubular instruments are used to extract the cells from the human body. The cells are then sent to a pathologist to determine the cell type. Using the pathological image analysis system, time is taken, where human factors are the main issues in using the system. In cancer treatment, both aspects of time and human error are important because both issues can lead to death. Also, the cell nucleus is key since the damaged cells do not tend to produce the circular cell nucleus. Therefore, the pathologist usually requires more time to identify whether it is a Low-Grade Squamous Intraepithelial Lesion (LSIL) or a High-Grade Squamous Intraepithelial Lesion (HSIL). Based on the limitations that occur, the paper aims to automate classification techniques based on the nucleus roundness value. Image processing via machine learning is an alternative method that can provide high accuracy with a short processing time. The system used multiple image algorithms to determine the circularity of the cell nucleus and provide additional data to determine the cell type. Based on calculations, detection accuracy was achieved up to 100%.