Deteksi Dini Kanker Kulit menggunakan K-NN dan Convolutional Neural Network

Penulis

Teresia R. Savera, Winsya H. Suryawan, Agung Wahyu Setiawan

Abstrak

Kanker kulit adalah salah satu jenis kanker yang dapat menyebabkan kematian sehingga diperlukan sebuah aplikasi perangkat lunak yang dapat digunakan untuk membantu melakukan deteksi dini kanker kulit dengan mudah. Sehingga diharapkan deteksi dini kanker kulit dapat terdeteksi lebih cepat. Pada penelitian ini terdapat dua metode yang digunakan untuk melakukan deteksi dini kanker kulit yaitu deteksi dengan klasifikasi secara regresi dan artificial neural network dengan arsitektur convolutional neural network. Akurasi yang diperoleh dengan menggunakan klasifikasi secara regresi adalah sebesar 75%. Sementara, akurasi deteksi yang didapatkan dengan menggunakan convolutional neural network adalah sebesar 76%. Hasil yang diperoleh dari kedua metoda ini masih dapat ditingkatkan pada penelitian lanjutan, yaitu dengan cara melakukan prapengolahan pada set data citra yang digunakan. Sehingga set data yang digunakan memiliki tingkat pencahayaan, sudut (pengambilan), serta ukuran citra yang sama. Apabila tersedia sumber daya komputasi yang besar, akan dilakukan penambahan jumlah citra yang digunakan, baik itu sebagai set data latih maupun uji.

 

Abstract

Skin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. In this study there were two methods used to detect skin cancer, namely detection by regression classification and detection by classifying using artificial neural networks with network convolutional architecture. Detection with regression classification gives an accuracy of 75%. While detection using convolutional neural networks gives an accuracy of 76%. These proposed early detection systems can be improved to increase the accuracy. For further development, several image processing techniques will be applied, such as contrast enhancement and color equalization. For future works, if there is more computational resource, more images can be used as dataset and implement the deep learning algorithm to improve the accuracy.


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Referensi


ARROYO, J.L.G. dan ZAPIRAIN, B.G., 2014. Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis. Computers in biology and medicine, 44, pp.144-157.

DALILA, F., ZOHRA, A., REDA, K. dan HOCINE, C., 2017. Segmentation and classification of melanoma and benign skin lesions. Optik, 140, pp.749-761.

HARANGI, B., 2018. Skin lesion classification with ensembles of deep convolutional neural networks. Journal of biomedical informatics, 86, pp.25-32.

JAIN, S. dan PISE, N., 2015. Computer aided melanoma skin cancer detection using image processing. Procedia Computer Science, 48, pp.735-740.

JAFARI, M.H., NASR-ESFAHANI, E., KARIMI, N., SOROUSHMEHR, S.R., SAMAVI, S. dan NAJARIAN, K., 2017. Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma. International journal of computer assisted radiology and surgery, 12(6), pp.1021-1030.

KASMI, R. dan MOKRANI, K., 2016. Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Processing, 10(6), pp.448-455.

KAYA, S., BAYRAKTAR, M., KOCKARA, S., METE, M., HALIC, T., FIELD, H.E. dan WONG, H.K., 2016, October. Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images. In BMC bioinformatics, 17(13), pp. 367. BioMed Central.

MARCHETTI, M.A., CODELLA, N.C., DUSZA, S.W., GUTMAN, D.A., HELBA, B., KALLOO, A., MISHRA, N., CARRERA, C., CELEBI, M.E., DEFAZIO, J.L. dan JAIMES, N., 2018. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. Journal of the American Academy of Dermatology, 78(2), pp.270-277.

MEHTA, P. dan SHAH, B., 2016. Review on techniques and steps of computer aided skin cancer diagnosis. Procedia Computer Science, 85, pp.309-316.

NASR-ESFAHANI, E., SAMAVI, S., KARIMI, N., SOROUSHMEHR, S.M.R., JAFARI, M.H., WARD, K. dan NAJARIAN, K., 2016, August. Melanoma detection by analysis of clinical images using convolutional neural network. In 2016 38th International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1373-1376. IEEE.

NAVARRO, F., ESCUDERO-VIÑOLO, M. and Bescós, J., 2018. Accurate segmentation and registration of skin lesion images to evaluate lesion change. IEEE journal of biomedical and health informatics, 23(2), pp.501-508.

PATHAN, S., PRABHU, K.G. dan SIDDALINGASWAMY, P.C., 2018. Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review. Biomedical Signal Processing and Control, 39, pp.237-262.

RAJINIKANTH, V., MADHAVARAJA, N., SATAPATHY, S.C. dan FERNANDES, S.L., 2017. Otsu's multi-thresholding and active contour snake model to segment dermoscopy images. Journal of Medical Imaging and Health Informatics, 7(8), pp.1837-1840.

RASTGOO, M., GARCIA, R., MOREL, O. dan MARZANI, F., 2015. Automatic differentiation of melanoma from dysplastic nevi. Computerized Medical Imaging and Graphics, 43, pp.44-52.

RUELA, M., BARATA, C., MARQUES, J.S. dan ROZEIRA, J., 2017. A system for the detection of melanomas in dermoscopy images using shape and symmetry features. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 5(2), pp.127-137.

ZORTEA, M., SCHOPF, T.R., THON, K., GEILHUFE, M., HINDBERG, K., KIRCHESCH, H., MØLLERSEN, K., SCHULZ, J., SKRØVSETH, S.O. dan GODTLIEBSEN, F., 2014. Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists. Artificial intelligence in medicine, 60(1), pp.13-26.




DOI: http://dx.doi.org/10.25126/jtiik.2020702602