Skin Cancer Detection from Smartphone Imagery using Convolutional Neural Network

Main Article Content

Kwankamon Dittakan
Piyawat Nulek
Korawit Prutsachainimmit


kin cancer is an abnormal growth of human skin cells that develop on the skin being exposed directly to ultraviolet radiation for an extended period of time. It is one of the most common health issues at a rapidly an alarming rate around the world, with 160,000 medical records reported annually. A significant number of records are in Europe, America, and New Zealand. In contrast, the least reported was in Thailand. Moreover, preventing the chance of developing skin cancer is the aim of this research. In this paper, we presented about to detect skin cancer from images using a Convolutional Neural Network (CNN) in one of the models in Deep Learning. In addition, the PAD-UFES-20 datasets are available from the Federal University of Esprito, Federative Republic of Brazil. The accuracy of results was predicted with 81.50% confidence.


Download data is not yet available.

Article Details

How to Cite
K. . Dittakan, P. Nulek, and K. . Prutsachainimmit, “Skin Cancer Detection from Smartphone Imagery using Convolutional Neural Network”, JIST, vol. 12, no. 2, pp. 73–86, Dec. 2022.
Academic Article: Multidisciplinary (Detail in Scope of Journal)


PuangthongKraipiboon, “มะเร็งผิวหนัง (Skin Cancer),”, Oct. 09, 2018.มะเร็งผิวหนัง (accessed Dec. 03, 2022).

MedThai, “มะเร็งผิวหนัง (Skin cancer) อาการ, สาเหตุ, การรักษา,”, Jul. 23, 2022.มะเร็งผิวหนัง (accessed Dec. 03, 2022).

ThaiHealth Promotion Foundation, “คนไทยป่วยมะเร็งผิวหนัง,”, Feb. 21, 2013.คนไทยป่วยมะเร็งผิวหนัง/ (accessed Dec. 03, 2022).

Faculty of Medicine Siriraj Hospital, Mahidol University, “การรักษามะเร็งผิวหนังด้วย Mohs SIRIRAJ ONLINE | Siriraj Hospital,”, Aug. 21, 2017. (accessed Dec. 03, 2022).

Niall McCarthy, “Infographic: The Nationalities Most Susceptible To Skin Cancer,” Statista, Jul. 26, 2018. (accessed Dec. 03, 2022).

Bangkok Hospital Pattaya, “โรงพยาบาลกรุงเทพพัทยา :International Hospital in Thailand,” (accessed Dec. 03, 2022).

Klongthom tech, “Machine Learning กับ Deep Learning,”, Oct. 10, 2020.กับ-deep-learning (accessed Dec. 03, 2022).

KraisakKesorn, “โครงข่ายประสาทเทียมอัจฉริยะ (Artificial Neuron Network),” Jan. 2021. Accessed: Dec. 03, 2022. [Online]. Available:

PradyaSin, “What is Convolution Neural Network,”, Aug. 16, 2019. (accessed Dec. 03, 2022).

A.-R. H. Ali, J. Li, and G. Yang, “Automating the ABCD Rule for Melanoma Detection: a Survey,” IEEE Access, vol. 8, no. 19658849, pp. 83333–83346, Apr. 2020, doi: 10.1109/access.2020.2991034.

S. Mane and S. Shinde, “A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images,” 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), no. 18618066, Aug. 2018, doi:10.1109/iccubea.2018.8697804.

A. G. C. Pacheco and R. A. Krohling, “The impact of patient clinical information on automated skin cancer detection,” Computers in Biology and Medicine, vol. 116, no. 103545, p. 103545, Jan. 2020, doi: 10.1016/j.compbiomed.2019.103545.

M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, and M. K. Islam, “An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models,” Machine Learning with Applications, vol. 5, no. 100036, p. 100036, Sep. 2021, doi:10.1016/j.mlwa.2021.100036.

I. Filali and M. Belkadi, “Multi-scale contrast based skin lesion segmentation in digital images,” Optik, vol. 185, pp. 794–811, May 2019, doi: 10.1016/j.ijleo.2019.04.022.

S. Medhat, H. Abdel-Galil, A. E. Aboutabl, and H. Saleh, “Skin cancer diagnosis using convolutional neural networks for smartphone images: A comparative study,” Journal of Radiation Research and Applied Sciences, vol. 15, no. 1, pp. 262–267, Mar. 2022, doi: 10.1016/j.jrras.2022.03.008.

W. Li, A. N. Joseph Raj, T. Tjahjadi, and Z. Zhuang, “Digital hair removal by deep learning for skin lesion segmentation,” Pattern Recognition, vol. 117, no. 107994, p. 107994, Sep. 2021, doi: 10.1016/j.patcog.2021.107994.

A. Mahbod, G. Schaefer, I. Ellinger, R. Ecker, A. Pitiot, and C. Wang, “Fusing fine-tuned deep features for skin lesion classification,” Computerized Medical Imaging and Graphics, vol. 71, pp. 19–29, Jan. 2019, doi: 10.1016/j.compmedimag.


I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, and N. Petkov, “MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images,” Expert Systems with Applications, vol.42, no.19, pp.6578–6585, Nov. 2015, doi: 10.1016/j.eswa.2015.04.034.

Sunny Shah, “DigitalHairRemoval,” GitHub, Nov. 30, 2022. (accessed Dec. 04, 2022).

SFU Professional Computer Science, “An Introduction to Convolutional Neural Network (CNN),”, Feb. 11, 2022. (accessed Dec. 03, 2022).

Sornpraram Xu, “รู้จักกับ EfficientDetหนึ่งในโมเดล Object Detection,”, Feb. 24, 2021.รู้จักกับ-efficientdet-หนึ่งในโมเดล-object-detection-cd0ac67f1f9b (accessed Dec. 03, 2022).

Erik Westphal, “A Machine Learning Method for Defect Detection and Visualization in Selective Laser Sintering based on Convolutional Neural Networks,”, Mar. 2021. (accessed Dec. 03, 2022).

Shamim Mahbub, “DenseNet121 Model Implementation,” Medium, Aug. 12, 2020. (accessed Dec. 04, 2022).

NatthawatPhongchit, “มาทำความรู้จัก ResNetกันดีกว่า,”, Mar. 18, 2020.มาทำความรู้จัก-resnet-กันดีกว่า-aec3a8c10793 (accessed Dec. 03, 2022).

Boyd BigData RPG, “เริ่มต้น Deep Learning Application ไปกับภาพวาดสไตล์ Doodle กันเถอะ !!,”, Nov. 23, 2018.เริ่มต้น-deep-learning-application-ไปกับภาพวาดสไตล์-doodle-กันเถอะ-c48561f2661b (accessed Dec. 03, 2022).

A. G. C. Pacheco et al., “PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones,” Data in Brief, vol. 32, no. 106221, Aug. 2020, doi: 10.1016/j.dib.2020.106221.