Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence
Abstract
Skin cancer is a type of cancer that can cause death, where skin cancer is included in the 15 common cancers that occur in Indonesia. The number of skin cancer sufferers was around 6,170 cases of non-melanoma skin cancer and 1,392 cases of melanoma skin cancer in 2018 in Indonesia. Therefore, research related to skin cancer classification is increasing. This is done as an initial step in detecting whether a lesion can be said to be cancerous or not. The deep learning approach has certainly shown promising results in carrying out classification, so this research proposes a deep learning-based method used for skin cancer classification. The proposed approach involves Convolutional Neural Networks with the ISIC 2017 dataset. The models used for skin cancer classification are InceptionV3, EfficientNetB0, ResNet50, MobileNetV2, and NASNetMobile. The highest accuracy of the single model produced reached 69.3% using the MobileNetV2 model. An ensemble model combining the five models was also tested and produced the highest accuracy compared to other single models with an accuracy result of 80.6%.
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D. Raval and J. N. Undavia, “A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images,” Healthcare Analytics, vol. 3, no. May, p. 100199, 2023, doi: 10.1016/j.health.2023.100199.
N. S. Alkolifi Alenezi, “A Method of Skin Disease Detection Using Image Processing and Machine Learning,” Procedia Comput Sci, vol. 163, pp. 85–92, 2019, doi: 10.1016/j.procs.2019.12.090.
L. S. Fonacier, S. C. Dreskin, and D. Y. M. Leung, “Allergic skin diseases,” Journal of Allergy and Clinical Immunology, vol. 125, no. 2 SUPPL. 2, pp. S138–S149, 2010, doi: 10.1016/j.jaci.2009.05.039.
S. Saravanan, B. Heshma, A. V. Ashma Shanofer, and R. Vanithamani, “Skin cancer detection using dermoscope images,” Mater Today Proc, vol. 33, pp. 4823–4827, 2020, doi: 10.1016/j.matpr.2020.08.388.
M. Ahammed, M. Al Mamun, and M. S. Uddin, “A machine learning approach for skin disease detection and classification using image segmentation,” Healthcare Analytics, vol. 2, no. April, p. 100122, 2022, doi: 10.1016/j.health.2022.100122.
I. C. C. Community, “SEKILAS KANKER KULIT,” Indonesia Cancer Care Community. Accessed: Dec. 24, 2023. [Online]. Available: https://iccc.id/sekilas-kanker-kulit
N. H. Khan et al., “Skin cancer biology and barriers to treatment: Recent applications of polymeric micro/nanostructures,” J Adv Res, vol. 36, pp. 223–247, 2022, doi: 10.1016/j.jare.2021.06.014.
V. Venugopal, N. I. Raj, M. K. Nath, and N. Stephen, “A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images,” Decision Analytics Journal, vol. 8, no. December 2022, p. 100278, 2023, doi: 10.1016/j.dajour.2023.100278.
T. Shanthi, R. S. Sabeenian, and R. Anand, “Automatic diagnosis of skin diseases using convolution neural network,” Microprocess Microsyst, vol. 76, p. 103074, 2020, doi: 10.1016/j.micpro.2020.103074.
A. Ghodekar and A. Kumar, “LightLeafNet: Lightweight and Efficient NASNetMobile Architecture for Tomato Leaf Disease Classification”, [Online]. Available: https://ssrn.com/abstract=4570347
E. Gomathi, M. Jayasheela, M. Thamarai, and M. Geetha, “Skin cancer detection using dual optimization based deep learning network,” Biomed Signal Process Control, vol. 84, no. February, p. 104968, 2023, doi: 10.1016/j.bspc.2023.104968.
K. Mimura and K. Nakamura, “Datasets for training and validating a deep learning-based system to detect microfossil fish teeth from slide images,” Data Brief, vol. 47, p. 108940, 2023, doi: 10.1016/j.dib.2023.108940.
A. Yilmaz, M. Kalebasi, Y. Samoylenko, M. E. Guvenilir, and H. Uvet, “Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset,” 2021, [Online]. Available: http://arxiv.org/abs/2110.12270
T. Khater, S. Ansari, S. Mahmoud, A. Hussain, and H. Tawfik, “Skin cancer classification using explainable artificial intelligence on pre-extracted image features,” Intelligent Systems with Applications, vol. 20, no. September, p. 200275, 2023, doi: 10.1016/j.iswa.2023.200275.
A. Lembhe, P. Motarwar, R. Patil, and S. Elias, “Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network,” Procedia Comput Sci, vol. 218, pp. 164–173, 2022, doi: 10.1016/j.procs.2022.12.412.
M. Valizadeh and S. J. Wolff, “Convolutional Neural Network applications in additive manufacturing: A review,” Advances in Industrial and Manufacturing Engineering, vol. 4, no. October 2021, p. 100072, 2022, doi: 10.1016/j.aime.2022.100072.
R. A. Pengestu, B. Rahmat, and F. T. Anggraeni, “Implementasi algoritma CNN untuk klasifikasi citra lahan dan perhitungan luas,” Jurnal Informatika dan Sistem Informasi (JIFoSI), vol. 1, no. 1, pp. 166–174, 2020.
A. Subeesh et al., “Deep convolutional neural network models for weed detection in polyhouse grown bell peppers,” Artificial Intelligence in Agriculture, vol. 6, pp. 47–54, 2022, doi: 10.1016/j.aiia.2022.01.002.
S. Yeşilmen and B. Tatar, “Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production,” Case Studies in Construction Materials, vol. 17, no. August, 2022, doi: 10.1016/j.cscm.2022.e01372.
A. Rafay and W. Hussain, “EfficientSkinDis: An EfficientNet-based classification model for a large manually curated dataset of 31 skin diseases,” Biomed Signal Process Control, vol. 85, no. December 2022, p. 104869, 2023, doi: 10.1016/j.bspc.2023.104869.
E. Pintelas, I. E. Livieris, S. Kotsiantis, and P. Pintelas, “A multi-view-CNN framework for deep representation learning in image classification,” Computer Vision and Image Understanding, vol. 232, no. January, p. 103687, 2023, doi: 10.1016/j.cviu.2023.103687.
J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Deep learning in computer vision: A critical review of emerging techniques and application scenarios,” Machine Learning with Applications, vol. 6, no. August, p. 100134, 2021, doi: 10.1016/j.mlwa.2021.100134.
R. Sadik, A. Majumder, A. A. Biswas, B. Ahammad, and M. M. Rahman, “An in-depth analysis of Convolutional Neural Network architectures with transfer learning for skin disease diagnosis,” Healthcare Analytics, vol. 3, no. November 2022, p. 100143, 2023, doi: 10.1016/j.health.2023.100143.
M. A. Ganaie, M. Hu, A. K. Malik, M. Tanveer, and P. N. Suganthan, “Ensemble deep learning: A review,” Eng Appl Artif Intell, vol. 115, no. March, p. 105151, 2022, doi: 10.1016/j.engappai.2022.105151.
S. Agarwal and C. R. Chowdary, “A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection,” Expert Syst Appl, vol. 146, 2020, doi: 10.1016/j.eswa.2019.113160.
C. Croux, K. Joossens, and A. Lemmens, “Trimmed bagging,” Comput Stat Data Anal, vol. 52, no. 1, pp. 362–368, 2007, doi: 10.1016/j.csda.2007.06.012.
S. Asadi and S. E. Roshan, “A bi-objective optimization method to produce a near-optimal number of classifiers and increase diversity in Bagging,” Knowl Based Syst, vol. 213, p. 106656, 2021, doi: 10.1016/j.knosys.2020.106656.
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