KLASIFIKASI TIPE SEL NORMAL/ABNORMAL BERDASARKAN CITRA PAP-SMEAR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

The classification of cell types plays an essential role in monitoring the growth of cancer cells. One of the methods to determine the cancer type is to analyze the pap-smear images manually. Nevertheless, the manual analysis of pap-smear images by the expert has several limitations, such as time-consuming and prone to misdiagnosis. For reducing the risks, it requires the automatic classification of cell types based on pap-smear images. This study utilizes the convolutional neural network (CNN) architectures to automatically classify the cell type into two-class categories (normal/abnormal) based on three features. These features, such as the local binary pattern, gray level co-occurrence matrix, and shape features, are extracted from pap-smear images. This study shows the performance of CNN achieved the maximum accuracy of 99.98%, 100.0%, 99.78% in training, validation, and testing data. Our approach also outperforms the performance of the baseline methods.    Keywords : CNN, Classification, Cell, Neural Network, Pap-smea

Analysis of Human Cervical Cell Images from Pap Smears for Classification

Cervical cancer is the fourth most commonly occurring cancer in women. It can be prevented by regular screenings to find precancerous cells through the Papanicolaou Smear Test and microscopy which can show cellular abnormalities. However, the manual microscopic screening for the nuclear abnormalities is subjective and prone to error, making automated detection a necessity. This study aims to quantify the nuclear features related to shape characteristics of normal and abnormal cells from pap smear images and examine potential detection of multi classes. Using the ground truth images of normal and abnormal cells we extracted the nuclear shape features that corresponded to the classified cells such as normal and three categories of abnormal: mild, moderate and severe; that is four classes. The dataset of the nuclear shape features were visually plotted as a heat map and bubble plots using the ground truth or known predetermined labelled normal, mild-, moderate- or severe abnormal cells, and also without any such labeling. By clustering, 78 - 89% of the cells were successfully matched with the ground truth. Further, we found that more than 4 classes were obtained. In conclusion, by data visualization techniques we can classify precancerous cells

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results