Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Ummi Athiyah, Arif Wirawan Muhammad, Ahmad Azhari


Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure.

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N. Sengar, N. Mishra, M. K. Dutta, J. Prinosil, and R. Burget, “Grading of colorectal cancer using histology images,” 2016 39th Int. Conf. Telecommun. Signal Process. TSP 2016, pp. 529–532, 2016, doi: 10.1109/TSP.2016.7760936.

A. Ratheesh, P. Soman, M. Revathy Nair, R. G. Devika, and R. P. Aneesh, “Advanced algorithm for polyp detection using depth segmentation in colon endoscopy,” 2016 Int. Conf. Commun. Syst. Networks, ComNet 2016, no. July, pp. 179–183, 2017, doi: 10.1109/CSN.2016.7824010.

U. Athiyah, I. Muhimmah, and E. Marfianti, “Ekstraksi Ciri Polip dan Pendarahan Berdasarkan Citra Endoskopi Kolorektal,” J. Inform. J. Pengemb. IT, vol. 3, no. 1, pp. 81–85, 2018.

Y. Shin, H. A. Qadir, and I. Balasingham, “Abnormal colon polyp image synthesis using conditional adversarial networks for improved detection performance,” IEEE Access, vol. 6, pp. 56007–56017, 2018, doi: 10.1109/ACCESS.2018.2872717.

X. Wei, J. Xie, W. He, M. Min, Z. Ma, and J. Guo, “Quantitative Comparisons of Linked Color Imaging and White-Light Colonoscopy for Colorectal Polyp Analysis,” Proc. 2018 6th IEEE Int. Conf. Netw. Infrastruct. Digit. Content, IC-NIDC 2018, pp. 140–144, 2018, doi: 10.1109/ICNIDC.2018.8525753.

G. Tarik, A. Khalid, K. Jamal, and D. A. Benajah, “Polyps’s region of interest detection in colonoscopy images by using clustering segmentation and region growing,” Colloq. Inf. Sci. Technol. Cist, pp. 455–459, 2017, doi: 10.1109/CIST.2016.7805090.

O. Bardhi, D. Sierra-Sosa, B. Garcia-Zapirain, and A. Elmaghraby, “Automatic colon polyp detection using Convolutional encoder-decoder model,” 2017 IEEE Int. Symp. Signal Process. Inf. Technol. ISSPIT 2017, pp. 445–448, 2018, doi: 10.1109/ISSPIT.2017.8388684.

Q. Li et al., “Colorectal polyp segmentation using a fully convolutional neural network,” Proc. - 2017 10th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2017, vol. 2018-Janua, pp. 1–5, 2018, doi: 10.1109/CISP-BMEI.2017.8301980.

I. O. Petre and C. Buiu, “A colon cancer microarray analysis technique,” 2017 E-Health Bioeng. Conf. EHB 2017, pp. 265–268, 2017, doi: 10.1109/EHB.2017.7995412.

N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE Trans. Med. Imaging, vol. 35, no. 2, pp. 630–644, 2016, doi: 10.1109/TMI.2015.2487997.

Y. Hu et al., “Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography,” IEEE Trans. Med. Imaging, vol. 35, no. 6, pp. 1522–1531, 2016, doi: 10.1109/TMI.2016.2518958.

A. W. Muhammad, G. W. Sasmito, and I. Riadi, “Colorectal Polyp Detection Using Feedforward Neural Network with Image Feature Selection,” Proceeding - 2018 Int. Symp. Adv. Intell. Informatics Revolutionize Intell. Informatics Spectr. Humanit. SAIN 2018, pp. 26–31, 2019, doi: 10.1109/SAIN.2018.8673371.

S. Dutta, P. Sasmal, M. K. Bhuyan, and Y. Iwahori, “Automatic Segmentation of Polyps in Endoscopic Image Using Level-Set Formulation,” 2018 Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2018, pp. 1–5, 2018, doi: 10.1109/WiSPNET.2018.8538615.

J. Qu, N. Hiruta, K. Terai, H. Nosato, M. Murakawa, and H. Sakanashi, “Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks,” J. Healthc. Eng., vol. 2018, 2018, doi: 10.1155/2018/8961781.

A. K. Palit and D. Popovic, Computational Intelligence in Time Series Forecasting, Advances in Industrial Control, 2005.

Y. H. Hu and J.-N. Hwang, “Handbook of Neural Network Signal Processing.” CRC Press, London, United Kingdom, 2002.

L. C. Jain, “Recent Advances in Artificial Neural Networks,” Recent Adv. Artif. Neural Networks, 2018, doi: 10.1201/9781351076210.

A. Geron, Hands-On Machine Learing With Scikit-Learn & Tensor Flow. O’Reilly Media, 2017.

I. Riadi, Sunardi, and A. W. Muhammad, “DDoS Detection Using Artificial Neural Network Regarding Variation of Training Function,” Adv. Sci. Lett., vol. 24, no. 12, pp. 9163–9167, 2018, doi: 10.1166/asl.2018.12117.

I. Riadi, A. Wirawan, and S. -, “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. 248–252, 2017, doi: 10.14569/ijacsa.2017.080631.

A. Azhari, A. W. Muhammad, and C. F. M. Foozy, “Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection,” International Journal of Artificial Intelligence Research, vol. 4, no. 1, Feb. 2020.



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