Skin Cancer Detection from Smartphone Imagery using Convolutional Neural Network
Main Article Content
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.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
PuangthongKraipiboon, “มะเร็งผิวหนัง (Skin Cancer),” Haamor.com, Oct. 09, 2018. https://haamor.com/มะเร็งผิวหนัง (accessed Dec. 03, 2022).
MedThai, “มะเร็งผิวหนัง (Skin cancer) อาการ, สาเหตุ, การรักษา,” medthai.com, Jul. 23, 2022. https://medthai.com/มะเร็งผิวหนัง (accessed Dec. 03, 2022).
ThaiHealth Promotion Foundation, “คนไทยป่วยมะเร็งผิวหนัง,” thaihealth.or.th, Feb. 21, 2013. https://www.thaihealth.or.th/คนไทยป่วยมะเร็งผิวหนัง/ (accessed Dec. 03, 2022).
Faculty of Medicine Siriraj Hospital, Mahidol University, “การรักษามะเร็งผิวหนังด้วย Mohs SIRIRAJ ONLINE | Siriraj Hospital,” si.mahidol.ac.th, Aug. 21, 2017. https://www.si.mahidol.ac.th/siriraj_online/thai_version/Health_detail.asp?id=28 (accessed Dec. 03, 2022).
Niall McCarthy, “Infographic: The Nationalities Most Susceptible To Skin Cancer,” Statista, Jul. 26, 2018. https://www.statista.com/chart/14872/the-nationalities-most-susceptible-to-skin-cancer/ (accessed Dec. 03, 2022).
Bangkok Hospital Pattaya, “โรงพยาบาลกรุงเทพพัทยา :International Hospital in Thailand,”bangkokpattayahospital.com. https://www.bangkokpattayahospital.com/th/ (accessed Dec. 03, 2022).
Klongthom tech, “Machine Learning กับ Deep Learning,” maggang.com, Oct. 10, 2020. https://klongthomtech.maggang.com/machine-learning-กับ-deep-learning (accessed Dec. 03, 2022).
KraisakKesorn, “โครงข่ายประสาทเทียมอัจฉริยะ (Artificial Neuron Network),” Jan. 2021. Accessed: Dec. 03, 2022. [Online]. Available: https://csit.nu.ac.th/kraisak/ds/ds/chapter07/Chapter07.pdf
PradyaSin, “What is Convolution Neural Network,” medium.com, Aug. 16, 2019. https://medium.com/@pradyasin/what-is-convolution-neural-network-bf2e525089f5 (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. https://github.com/sunnyshah2894/DigitalHairRemoval (accessed Dec. 04, 2022).
SFU Professional Computer Science, “An Introduction to Convolutional Neural Network (CNN),” medium.com, Feb. 11, 2022.https://medium.com/sfu-cspmp/an-introduction-to-convolutional-neural-network-cnn-207cdb53db97 (accessed Dec. 03, 2022).
Sornpraram Xu, “รู้จักกับ EfficientDetหนึ่งในโมเดล Object Detection,” medium.com, Feb. 24, 2021. https://medium.com/super-ai-engineer/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,” researchgate.ne, Mar. 2021.https://www.researchgate.net/figure/Xception-CNN-architecture-for-the-detection-and-classification-of-powder-bed-defects-at_fig3_350319854 (accessed Dec. 03, 2022).
Shamim Mahbub, “DenseNet121 Model Implementation,” Medium, Aug. 12, 2020. https://medium.com/@shamimmahbub230/densenet121-model-implementation-7c403c7e521b (accessed Dec. 04, 2022).
NatthawatPhongchit, “มาทำความรู้จัก ResNetกันดีกว่า,” medium.com, Mar. 18, 2020. https://medium.com/@natthawatphongchit/มาทำความรู้จัก-resnet-กันดีกว่า-aec3a8c10793 (accessed Dec. 03, 2022).
Boyd BigData RPG, “เริ่มต้น Deep Learning Application ไปกับภาพวาดสไตล์ Doodle กันเถอะ !!,” medium.com, Nov. 23, 2018. https://medium.com/bigdataeng/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.