Pengenalan Polip Usus Menggunakan Neural Network

Ummi Athiyah(1*)
(1) Institut Teknologi Telkom Purwokerto
(*) Corresponding Author
DOI : 10.35889/progresif.v19i1.1067

Abstract

Colorectal cancer is one of the most common types of cancer experienced by the world's population and causes approximately 700,000 deaths each year. Early detection is one of the keys to detecting polyps before they transform into malignant colorectal cancer. Polyp detection is a complicated matter because polyp detection involves many factors. This research attempts to make new improvisations in the polyp detection process in terms of the pre-processing stage and classification stage by utilizing statistical selection techniques for a collection of bag of features and the feedforward neural network classifier function. Based on the experiments that have been carried out, it was found that the proposed method was able to provide the highest accuracy in recognizing polyps of 97.85%.

Keywords: Cancer; Colorectal; Polyp; Neural Network  

 

Abstrak

Kanker kolorektal adalah salah satu jenis penyakit kanker yang sering dialami oleh penduduk dunia dan menjadi penyebab kurang lebih 700,000 kematian setiap tahunnya. Tindakan deteksi dini merupakan salah satu kunci untuk mendeteksi adanya polip, sebelum bertransformasi menjadi kanker kolorektal ganas. Deteksi polip merupakan suatu hal yang rumit karena deteksi polip melibatkan banyak faktor. Penelitian ini melakukan improvisasi baru dalam proses deteksi polip dari sisi pre-processing stage serta classification stage dengan mengutilisasi teknik seleksi statistik terhadap kumpulan bag of features serta fungsi classifier feedforward neural network. Berdasarkan percobaan yang telah dilaksanakan didapatkan bahwa metode yang diusulkan mampu memberikan nilai akurasi tertinggi dalam mengenali polip sebesar 97.85%.

Kata kunci: Kanker; Kolorektal; Polyp; Neural Network

References


A. Akutekwe and H. Seker, “Particle Swarm Optimization-Based Bio-Network Discovery Method for the Diagnosis of Colorectal Cancer,” Proc. - 2014 IEEE Int. Conf. Bioinforma. Biomed. IEEE BIBM 2014, pp. 8–13, 2014, doi: 10.1109/BIBM.2014.6999241.

R. Zhang et al., “Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features from Nonmedical Domain,” IEEE J. Biomed. Heal. Informatics, vol. 21, no. 1, pp. 41–47, 2017, doi: 10.1109/JBHI.2016.2635662.

P. Mesejo et al., “Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy,” IEEE Trans. Med. Imaging, vol. 35, no. 9, pp. 2051–2063, 2016, doi: 10.1109/TMI.2016.2547947.

D. Perez and Y. Shen, “Deep Learning for Pulmonary Nodule CT Image Retrieval - An Online Assistance System for Novice Radiologists,” no. Lidc, pp. 1112–1121, 2017, doi: 10.1109/ICDMW.2017.158.

A. Banwari, N. Sengar, M. K. Dutta, and C. M. Travieso, “Automated Segmentation of Colon Gland Using Histology Images,” 2016 9th Int. Conf. Contemp. Comput. IC3 2016, 2017, doi: 10.1109/IC3.2016.7880223.

O. Romain et al., “Towards a Multimodal Wireless Video Capsule for Detection of Colonic Polyps as Prevention of Colorectal Cancer,” 13th IEEE Int. Conf. Bioinforma. Bioeng., pp. 1–6, 2013, doi: 10.1109/BIBE.2013.6701670.

Y. Yuan, S. Member, D. Li, and M. Q. Meng, “Automatic Polyp Detection via A Novel Unified Bottom-up and Top-down Saliency Approach,” IEEE J. Biomed. Heal. Informatics Autom., vol. 2194, no. c, 2017, doi: 10.1109/JBHI.2017.2734329.

M. H. Soomrol et al., “Haralick’s Texture Analysis Applied To Colorectal T2- Weighted Mri: A Preliminary Study Of Significance For Cancer Evolution,” Proc. lASTED Int. Conf. Biomed. Eng. (BioMed 2017) Febr., pp. 16–19, 2017.

J. Bernal et al., “Comparative Validation of Polyp Detection Methods in Video Colonoscopy : Results From the MICCAI 2015 Endoscopic Vision Challenge,” IEEE Trans. Med. Imaging, vol. 36, no. 6, pp. 1231–1249, 2017.

F. Ciompi et al., “The Importance Of Stain Normalization In Colorectal Tissue Classification With Convolutional Networks,” Proc. - Int. Symp. Biomed. Imaging, pp. 160–163, 2017, doi: 10.1109/ISBI.2017.7950492.

Y. Ren, J. Ma, J. Xiong, Y. Chen, L. Lu, and J. Zhao, “Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 1, pp. 324-333, 2018, doi: 10.1109/JBHI.2018.2808199.

M. Biswas, S. Jana, S. Bhattacharya, and S. Aktar, “Discrete Wavelet Transform Based Colon Polyp Detection Using Synthesize Similarity Measure,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 8, pp. 1-6, 2017.

L. Yu, S. Member, H. Chen, S. Member, Q. Dou, and S. Member, “Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos,” IEEE J. Biomed. Heal. Informatics, vol. 21, no. 1, pp. 65–75, 2017.

A. Saribudak, H. Kucharavy, K. Hubbard, and M. U. Uyar, “Spatial Heterogeneity Analysis in Evaluation of Cell Viability and Apoptosis for Colorectal Cancer Cells.,” IEEE J. Transl. Eng. Heal. Med., vol. 4, no. 8, p. 4300209, 2016, doi: 10.1109/JTEHM.2016.2578331.

B. Taha, N. Werghi, and J. Dias, “Automatic Polyp Detection In Endoscopy Videos : A Survey,” Proc. lASTED Int. Conf. Biomed. Eng. (BioMed 201 7), no. February, pp. 233–240, 2017.

N. Pise and P. Kulkarni, “Algorithm Selection for Classification Problems,” SAI Comput. Conf. 2016, pp. 203–211, 2016, doi: 10.1109/SAI.2016.7555983.

M. Anthony and P. L. Bartlett, Neural Network Learning : Theoretical Foundations, First Edit. New York: Cambridge University Press, 2009.

H. Demuth, Neural Network Toolbox Users Guide, Sixth Ed., vol. 24, no. 1. Natick, Massachuset: The MathWorks, Inc, 2002. doi: 10.1016/j.neunet.2005.10.002.

I. Riadi, A. W. Muhammad, and Sunardi, “Network Packet Classification Using Neural Network Based on Training Function and Hidden Layer Neuron Number Variation,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 6, pp. 1–4, 2017.

Y. H. Hu and J.-N. Hwang, Handbook of Neural Network Signal Processing, First Edit. New York: CRC Press, 2002.

L. V. Fausset, Fundamental of Neural Networks Architectures, Algorithms, and Application. Englewood Cliffs, New York: Prentice-Hall, 1994.

I. Riadi, A. W. Muhammad, and Sunardi, “Neural Network-Based DDoS Detection Regarding Hidden Layer Variation,” J. Theor. Appl. Inf. Technol., vol. 95, pp. 1–9, 2017.

J. L. Semmlow, Biosignal and Medical Image Processing MATLAB-Based Applications (Signal Processing and Communications), First. Marcel Dekker, 2004.

A. Abebe, J. Daniels, J. W. McKean, and J. A. Kapenga, Statistics and Data Analysis. Michigan: Western Michigan University, 2001.

L. V. Fausset, Fundamental of Neural Networks Architectures, Algorithms, and

Application. Englewood Cliffs, New York: Prentice-Hall, 1994.

M. Anthony and P. L. Bartlett, Neural Network Learning : Theoretical Foundations, First

Edit. New York: Cambridge University Press, 2009.


How To Cite This :

Refbacks

  • There are currently no refbacks.