Cervical Cancer Identification Based Texture Analysis Using GLCM-KELM on Colposcopy Data

Novitasari, Dian Candra Rini; Asyhar, Ahmad Hanif; Thohir, Muhammad; Arifin, Ahmad Zaenal; Mu'jizah, Hanimatim; Foeady, Ahmad Zoebad

doi:10.1109/icaiic48513.2020.9065252

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…In a study conducted by [34], GLRLM had better accuracy quality than the GLCM method. The future work is by applying ELM development methods, namely the Kernel Extreme Learning Machine (K-ELM) [35] and Multi-Layer Extreme Learning Machine (MLLEM) [36].…”

Section: Resultsmentioning

confidence: 99%

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Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.

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Section: Resultsmentioning

confidence: 99%

“…Table 2 shows the data used in this study. The data was taken on 03 April 2020 from [32][33][34][35]. The data increased every week so that when retrieving at different times the number of data increased.…”

Section: Datamentioning

confidence: 99%

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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“…Image data is divided into test data and training data using the k-fold crossvalidation method. The classification results are tested using a confusion matrix with the accuracy, sensitivity, and specificity parameters to determine the method's accuracy in the image data used [33].…”

Section: Classificationmentioning

confidence: 99%

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Mu'jizah

Novitasari

2021

J. Teknol. dan Sist. Komput

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Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

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“…Therefore, better performance of nucleus detection method is needed to provide a good data for classification in determination of cervical cancer. [34] 143 whole slide images -93% --Novitasari et al [35] Colposcopy data 95% ---…”

Section: Introductionmentioning

confidence: 99%

Edge Enhancement and Detection Approach on Cervical Cytology Images

Alias

Mustafa

Jamlos

et al. 2022

ARASET

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Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Method used in this study is the contrast enhancement technique for pre-processing and edge detection-based for segmentation of the nucleus. In this study, the average performance results of the method showed an accuracy of 96.99% in the seven-class problem using Herlev dataset. The present finding also support this study which concluded the results of accuracy achieved for the algorithm used for nucleus detection is improved by 6.15% when comparing to previous work. The accuracy value is in the lines of earlier literature that achieved accuracy of the approach used above 90% for seven class of cells. The major feature of the suggested approach is an improvement in the ability to anticipate which cells are aberrant and which are normal. Adding more classifiers could improve the suggested system even further. Therefore, a cervical cancer screening system might utilize this framework to identify women who have precancerous lesions.

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Cervical Cancer Identification Based Texture Analysis Using GLCM-KELM on Colposcopy Data

Cited by 13 publications

References 28 publications

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Edge Enhancement and Detection Approach on Cervical Cytology Images

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