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Journal of Cybersecurity and Information Management
Volume 2 , Issue 2, PP: 58-67 , 2020 | Cite this article as | XML | Html |PDF

Title

An Efficient Machine Learning based Cervical Cancer Detection and Classification

  Ahmed N. Al Masri 1 * ,   Hamam Mokayed 2

1  American University in the Emirates, Dubai, United Arab Emirates
    (ahmed.almasri@aue.ae)

2  LTU University of Technology, Sweden
    (Hamam.mokayed@ltu.se)


Doi   :   https://doi.org/10.54216/JCIM.020203


Abstract :

Cervical cancer (CC) is the fourth commonly occurring cancer among females over the globe. It accounts for 7.9% of woman cancer as identified by world health organization (WHO). The most important reason for increased mortality due to cervical cancer is the deficiency of effective initial treatment. The asymptomatic nature is a main problem faced in the analysis of CC from initial stage. Recently, computer aided diagnosis (CAD) model has gained significant attention in the disease diagnostic process. At the same time, machine learning (ML) finds its use in several medical applications and is utilized as classifier for the initial detection of cancerous cells occurs from cervix area of uterus. With this motivation, this study introduces an intelligent ML based CAD (IML-CAD) technique to classify cervix cancer. The IML-CAD technique involves different stages of operations to detect and classify the cancerous cervix cells. In addition, the IML-CAD technique involves histogram based segmentation to determine the affected regions. Moreover, local binary patterns (LBP) based feature extractor and least squares support vector machine (LS-SVM) based classifier is designed for CC classification. To showcase the better performance of the IML-CAD technique, a series of simulations is performed and the experimental results highlighted the superior performance of the IML-CAD technique over the other techniques.

Keywords :

Machine learning , Cervical cancer , Pap Smear images , CAD model , Image classification

References :

[1]     Dong, N., Zhao, L., Wu, C. H., & Chang, J. F. (2020). Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing, 93, 106311.

[2]     Yusufaly, T. I., Kallis, K., Simon, A., Mayadev, J., Yashar, C. M., Einck, J. P.,... & Meyers, S. M. (2020). A knowledge-based organ dose prediction tool for brachytherapy treatment planning of patients with cervical cancer. Brachytherapy, 19 (5), 624-634.

[3]     Shao, J., Zhang, Z., Liu, H., Song, Y., Yan, Z., Wang, X., & Hou, Z. (2020). DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction. Computers in biology and medicine, 118, 103634.

[4]     Zhang, T., Luo, Y. M., Li, P., Liu, P. Z., Du, Y. Z., Sun, P.,... & Xue, H. (2020). Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images. Biomedical Signal Processing and Control, 55, 101566.

[5]     Hua, W., Xiao, T., Jiang, X., Liu, Z., Wang, M., Zheng, H., & Wang, S. (2020). Lymph-vascular space invasion prediction in cervical cancer: exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI. Biomedical Signal Processing and Control, 58, 101869.

[6]     Lu, J., Song, E., Ghoneim, A., & Alrashoud, M. (2020). Machine learning for assisting cervical cancer diagnosis: An ensemble approach. Future Generation Computer Systems, 106, 199-205.

[7]     Ghoneim, A., Muhammad, G., & Hossain, M. S. (2020). Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Generation Computer Systems, 102, 643-649.

[8]     Nayak, M., Das, S., Bhanja, U., & Senapati, M. R. (2020). Elephant herding optimization technique based neural network for cancer prediction. Informatics in Medicine Unlocked, 21, 100445.

[9]     Kim, S. I., Lee, S., Choi, C. H., Lee, M., Kim, J. W., & Kim, Y. B. (2020). Prediction of disease recurrence according to surgical approach of primary radical hysterectomy in patients with early-stage cervical cancer using machine learning methods. Gynecologic Oncology, 159, 185-186.

[10]  Agus Pratondo, Chee-Kong Chui, Sim-Heng Ong (2017). Integrating machine learning with region-based active contour models in medical image segmentation, Journal of Visual Communication and Image Representation, 43, 1-9, 2017 

[11]  Deepa, B., & Sumithra, M. G. (2019). An intensity factorized thresholding based segmentation technique with gradient discrete wavelet fusion for diagnosing stroke and tumor in brain MRI. Multidimensional Systems and Signal Processing, 30 (4), 2081-2112.

[12]  William, W., Ware, A., Basaza-Ejiri, A. H., & Obungoloch, J. (2018). A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Computer methods and programs in biomedicine, 164, 15-22.

[13]  Zhang, C., Leng, W., Sun, C., Lu, T., Chen, Z., Men, X.,... & Qin, J. (2018). Urine proteome profiling predicts lung cancer from control cases and other tumors. EBioMedicine, 30, 120-128.

[14]  Matsuo, K., Purushotham, S., Moeini, A., Li, G., Machida, H., Liu, Y., & Roman, L. D. (2017). A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer. American journal of obstetrics and gynecology, 217 (6), 703.

[15]  Iliyasu, A. M., & Fatichah, C. (2017). A quantum hybrid PSO combined with fuzzy k-NN approach to feature selection and cell classification in cervical cancer detection. Sensors, 17 (12), 2935.

[16]  Lu, J., Song, E., Ghoneim, A., & Alrashoud, M. (2020). Machine learning for assisting cervical cancer diagnosis: An ensemble approach. Future Generation Computer Systems, 106, 199-205.

[17]  Ijaz, M. F., Attique, M., & Son, Y. (2020). Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors, 20 (10), 2809.

[18]  Khamparia, A., Gupta, D., de Albuquerque, V. H. C., Sangaiah, A. K., & Jhaveri, R. H. (2020). Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning. The Journal of Supercomputing, 1-19.

[19]  Zhang, C. W., Jia, D. Y., Wu, N. K., Guo, Z. G., & Ge, H. R. (2021). Quantitative detection of cervical cancer based on time series information from smear images. Applied Soft Computing, 107791.

[20]  Rehman, A. U., Ali, N., Taj, I., Sajid, M., & Karimov, K. S. (2020). An Automatic Mass Screening System for Cervical Cancer Detection Based on Convolutional Neural Network. Mathematical Problems in Engineering, 2020.

[21]  Khan, K. A., Shanir, P. P., Khan, Y. U., & Farooq, O. (2020). A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy. Expert Systems with Applications, 140, 112895.

[22]  Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics, 15 (1), 41-51.

[23]  Tian, Z. (2020). Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM. Engineering Applications of Artificial Intelligence, 91, 103573.

[24]  Razavi, R., Bemani, A., Baghban, A., Mohammadi, A. H., & Habibzadeh, S. (2019). An insight into the estimation of fatty acid methyl ester based biodiesel properties using a LSSVM model. Fuel, 243, 133-141.

[25]  DTU/Herlev Pap Smear Database. (2008). http://mde-lab.aegean.gr/index.php/downloads 

 

 


Cite this Article as :
Style #
MLA Ahmed N. Al Masri, Hamam Mokayed. "An Efficient Machine Learning based Cervical Cancer Detection and Classification." Journal of Cybersecurity and Information Management, Vol. 2, No. 2, 2020 ,PP. 58-67 (Doi   :  https://doi.org/10.54216/JCIM.020203)
APA Ahmed N. Al Masri, Hamam Mokayed. (2020). An Efficient Machine Learning based Cervical Cancer Detection and Classification. Journal of Journal of Cybersecurity and Information Management, 2 ( 2 ), 58-67 (Doi   :  https://doi.org/10.54216/JCIM.020203)
Chicago Ahmed N. Al Masri, Hamam Mokayed. "An Efficient Machine Learning based Cervical Cancer Detection and Classification." Journal of Journal of Cybersecurity and Information Management, 2 no. 2 (2020): 58-67 (Doi   :  https://doi.org/10.54216/JCIM.020203)
Harvard Ahmed N. Al Masri, Hamam Mokayed. (2020). An Efficient Machine Learning based Cervical Cancer Detection and Classification. Journal of Journal of Cybersecurity and Information Management, 2 ( 2 ), 58-67 (Doi   :  https://doi.org/10.54216/JCIM.020203)
Vancouver Ahmed N. Al Masri, Hamam Mokayed. An Efficient Machine Learning based Cervical Cancer Detection and Classification. Journal of Journal of Cybersecurity and Information Management, (2020); 2 ( 2 ): 58-67 (Doi   :  https://doi.org/10.54216/JCIM.020203)
IEEE Ahmed N. Al Masri, Hamam Mokayed, An Efficient Machine Learning based Cervical Cancer Detection and Classification, Journal of Journal of Cybersecurity and Information Management, Vol. 2 , No. 2 , (2020) : 58-67 (Doi   :  https://doi.org/10.54216/JCIM.020203)