A powerful deep learning method for skin cancer detection
Abstract
The authors discussed the issues posed by our study’s inability to accurately diagnose skin cancer and distinguish between various skin growths, especially without the aid of cutting-edge medical technology and highly skilled diagnosticians. To that purpose, the authors have developed a deep learning (DL) method that can recognise skin cancer from images. This study investigates the use of a convolutional neural network (CNN) and the Keras Sequential-application programming interface (API) to detect cancer in seven different types of chronic lesions. The researchers used the HAM10000 dataset, which is openly available. 10,015 skin growth images with annotations are included in this dataset. The authors used several data pre-processing techniques after reading the data but before training our model. The authors provide pre-trained data for comparison and reliability assessments. Examples of transfer learning models that are used for comparison include ResNet50, DenseNet121, and VGG11. In the area of skin growth classification for skin cancer diagnosis, this helps in the identification of improved DL application procedures. Over 97.12% of the time, our suggested model accurately predicts the sort of skin that will develop.
Keywords
Full Text:
PDFReferences
1. Hoshyar AN, Al-Jumaily A, Hoshyar AN. The beneficial techniques in preprocessing step of skin cancer detection system comparing. Procedia Computer Science 2014; 42: 25–31. doi: 10.1016/j.procs.2014.11.029
2. Lau HT, Al-Jumaily A. Automatically early detection of skin cancer: Study based on nueral netwok classification. In: Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition; 4–7 December 2009; Malacca, Malaysia. pp. 375–380.
3. Seeja RD, Suresh A. Deep learning based skin lesion segmentation and classification of melanoma using support vector machine (SVM). Asian Pacific Journal of Cancer Prevention 2019; 20(5): 1555–1561. doi: 10.31557/APJCP.2019.20.5.1555
4. Blot M, Cord M, Thome N. Max-min convolutional neural networks for image classification. In: Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP); 25–28 September 2016; Phoenix, AZ, USA. pp. 3678–3682.
5. Skin cancer. Available online: https://www.mayoclinic.org/diseases-conditions/skin-cancer/symptoms-causes/syc-20377605 (accessed on 6 January 2023).
6. World Health Organisation. Radiation: Ultraviolet (UV) radiation and skin cancer. Available online: https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer (accessed on 6 January 2023).
7. A shortage of oncologists. Available online: https://www.clearityfoundation.org/a-shortage-of-oncologists/ (accessed on 7 January 2023).
8. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115–118. doi: 10.1038/nature21056
9. Mendonca T, Ferreira PM, Marques JS, et al. PH2—A dermoscopic image database for research and benchmarking. In: Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS); 3–7 July 2013; Osaka, Japan. pp. 5437–5440.
10. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 2018; 5: 2018. doi: 10.1038/sdata.2018.161
11. Manne R, Kantheti S, Kantheti S. Classification of skin cancer using deep learning, convolutional neural networks-opportunities and vulnerabilities—A systematic review. International Journal for Modern Trends in Science and Technology 2020; 6(11): 101–108. doi: 10.46501/IJMTST061118
12. Thurnhofer-Hemsi K, Domínguez E. A convolutional neural network framework for accurate skin cancer detection. Neural Processing Letters 2021; 53(5): 3073–3093. doi: 10.1007/s11063-020-10364-y
13. Rahi MMI, Khan FT, Mahtab MT, et al. Detection of skin cancer using deep neural networks. In: Proceedings of the 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE); 9–11 December 2019; Melbourne, VIC, Australia. pp. 1–7.
14. Mohamed EH, El-Behaidy WH. Enhanced skin lesions classification using deep convolutional networks. In: Proceedings of the 2019 IEEE 9th International Conference on Intelligent Computing and Information Systems (ICICIS); 8–10 December 2019; Cairo, Egypt. pp. 180–188.
15. Kousis I, Perikos I, Hatzilygeroudis I, Virvou M. Deep learning methods for accurate skin cancer recognition and mobile application. Electronics 2022; 11(9): 1294. doi: 10.3390/electronics11091294
16. Suárez-Paniagua V, Segura-Bedmar I. Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction. BMC Bioinformatics 2018; 19: 209. doi: 10.1186/s12859-018-2195-1
17. Kingma DP, Ba JL. Adam: A method for stochastic optimization. In: Proceedings of the ICLR 2015 International Conference on Learning Representations (ICLR); 7–9 May 2015; San Diego, CA, USA.
18. Indriyani T, Nurmuslimah S, Taufiqurrahman A, et al. Steganography on Color Images Using Least Significant Bit (LSB) Method. In: Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022); 29–30 November 2022; Jember, Indonesia.
19. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 21–26 July 2017; Honolulu, HI, USA. pp. 2261–2269.
20. VGG-11 architecture. Available online: https://iq.opengenus.org/vgg-11/ (accessed 7 January 2023).
DOI: https://doi.org/10.32629/jai.v7i1.1156
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Ahmed Mahdi Obaid, Aws Saad Shawkat, Nazar Salih Abdulhussein
License URL: https://creativecommons.org/licenses/by-nc/4.0/