Cervical single cell of squamous intraepithelial lesion classification using shape features and extreme learning machine

Cervical cancer is an abnormal growth of cells found on the cervix. In general, cervical cancer is identified early by doing a pap smear test. However, this examination is still manually performed by doctors and the results are still subjective. Therefore, this study aims to determine the classification of Squamous Intraepithelial Lesion automatically from cervical single cells. The classification of those Squamous Intraepithelial Lesion includes normal cervical cells, Low-Grade Squamous Intraepithelial Lesion (LSIL), and High-Grade Squamous Intraepithelial Lesion (HSIL). We used Extreme Learning Machine (ELM) as a classifier and tried to compare the ELM's performances with Backpropagation Neural Network method. We used 225 data and 3 classes include normal, LSIL, and HSIL. The classification was carried out by manual cropping and segmentation as the image pre-processing and the feature extraction was based on shape features consisting of Circularity, Semi Major and Minor Axis Length, Equivalent Diameter, Average Radius, and Compactness. This study concluded that Squamous Intraepithelial Lesion classification by using ELM had better performances than Backpropagation Neural Network. The highest accuracy result of 96.67% was obtained in Backpropagation training, while the highest accuracy in ELM's training was 100% when both methods were tried by using 225 data.