Optimization of Melanoma Skin Cancer Detection with the Convolutional Neural Network

Harming Puja Kekal; Daniati Uki Eka Saputri

doi:10.37034/medinftech.v1i2.10

Authors

Harming Puja Kekal Universitas Nusa Mandiri Jakarta
Daniati Uki Eka Saputri Universitas Nusa Mandiri Jakarta

DOI:

https://doi.org/10.37034/medinftech.v1i2.10

Keywords:

Neural Network, Deep Learning, Convolutional Neural Network (CNN), Skin Cancer, MobileNet

Abstract

Currently, skin cancer is a very dangerous disease for humans. Skin cancer is classified into many types such as Melanoma, Basal and Squamous cell carcinoma. In all types of cancer, melanoma is the most dangerous and unpredictable disease. Detection of melanoma cancer at an early stage is useful for effective treatment and can be used to classify types of melanoma cancer. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to assist the medical and medical world in analyzing images precisely and accurately. The method used in this research is Convolutional Neural Network (CNN) with MobileNet model architecture. Skin cancer detection consists of five important stages, namely image database collection, preprocessing methods, augmentation data, model training and model evaluation. This evaluation was carried out using the MobileNet method with an accuracy of 88%.

Downloads

Download data is not yet available.

References

N. Codella et al., “International Skin Imaging Collaboration ( ISIC ),” pp. 1–12, 2018,2021.

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2018,” CA. Cancer J. Clin., vol. 68, no. 1, pp. 7–30, 2021.

T. L. D. Munthe, “Klasifikasi Citra Kanker Kulit Berdasarkan Tingkat Keganasan Kanker Pada Melanosit Menggunakan Deep Convolutional Neural Network (DCNN),” Repos. Institusi Univ. Sumatera Utara, pp. 44–48, 2021.

R. Suganya, “An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images,” 2016 Int. Conf. Recent Trends Inf. Technol. ICRTIT 2016, 2021.

M. U. Rehman, S. H. Khan, S. M. Danish Rizvi, Z. Abbas, and A. Zafar, “Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network,” 2018 2nd Int. Conf. Eng. Innov. ICEI 2018, pp. 81–85, 2021.

Y. Ji, X. Li, G. Zhang, D. Lin, and H. Chen, “Automatic Skin Lesion Segmentation by Feature Aggregation Convolutional Neural Network,” 2021.

Y. Guo and A. S. Ashour, “Multiple convolutional neural network for skin dermoscopic image classification,” arXiv, pp. 365–369, 2021.

X. Dai, I. Spasic, B. Meyer, S. Chapman, and F. Andres, “Machine learning on mobile: An on-device inference app for skin cancer detection,” 2019 4th Int. Conf. Fog Mob. Edge Comput. FMEC 2019, pp. 301–305, 2019.

S. Bassi and A. Gomekar, “Deep Learning Diagnosis of Pigmented Skin Lesions,” 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1–6, 2021.

S. Albawi, T. A. M. Mohammed, and S. Alzawi, “Layers of a Convolutional Neural Network,” Ieee, 2017.

Institute of Electrical and Electronics Engineers, “2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA 2017) : March 10-12, 2017, Beijing, China.,” pp. 721–724, 2021.

G. S. Jayalakshmi and V. S. Kumar, “Performance analysis of convolutional neural network (CNN) based cancerous skin lesion detection system,” ICCIDS 2019 - 2nd Int. Conf. Comput. Intell. Data Sci. Proc., pp. 1–6, 2021.

E. Jana, R. Subban, and S. Saraswathi, “Research on Skin Cancer Cell Detection Using Image Processing,” 2017 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2017, 2021.

K. Pai and A. Giridharan, “Convolutional Neural Networks for classifying skin lesions,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2019-Octob, pp. 1794–1796, 2021.

A. Budhiman, S. Suyanto, and A. Arifianto, “Melanoma Cancer Classification Using ResNet with Data Augmentation,” 2019 2nd Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2019, pp. 17– 20, 2021.

E. H. Mohamed and W. H. El-Behaidy, “Enhanced Skin Lesions Classification Using Deep Convolutional Networks,” Proc. - 2019 IEEE 9th Int. Conf. Intell. Comput. Inf. Syst. ICICIS 2019, pp. 180–188, 2021.

B. Harangi, A. Baran, and A. Hajdu, “Classification of Skin Lesions Using An Ensemble of Deep Neural Networks,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2018- July, pp. 2575–2578, 2018,2021

V. Singh and I. Nwogu, “Analyzing Skin Lesions in Dermoscopy Images Using Convolutional Neural Networks,” Proc. - 2018 IEEE Int. Conf. Syst. Man, Cybern. SMC 2018, pp. 4035–4040, 2021.

A. Ech-Cherif, M. Misbhauddin, and M. Ech-Cherif, “Deep Neural Network Based Mobile Dermoscopy Application for Triaging Skin Cancer Detection,” 2nd Int. Conf. Comput. Appl. Inf. Secur. ICCAIS 2019, pp. 1–6, 2021.

A. H. Shahin, A. Kamal, and M. A. Elattar, “Deep Ensemble Learning for Skin Lesion Classification from Dermoscopic Images,” 2018 9th Cairo Int. Biomed. Eng. Conf. CIBEC 2018 - Proc., pp. 150–153, 2019.

S. Kaymak, P. Esmaili, and A. Serener, “Deep Learning for TwoStep Classification of Malignant Pigmented Skin Lesions,” 2018 14th Symp. Neural Networks Appl. NEUREL 2018, pp. 1–6, 2021.

R. Jain, P. Nagrath, G. Kataria, V. Sirish Kaushik, and D. Jude Hemanth, “Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning,” Meas. J. Int. Meas. Confed., vol. 165, p. 108046, 2021.