Deep Learning Techniques For Skin Cancer Detection And Diagnosis

Gagah Dwiki Putra Aryono(1*), Alisa Audina(2), Sigit Auliana(3),

(1) Universitas Bina Bangsa, Indonesia
(2) Universitas Bina Bangsa, Indonesia
(3) Universitas Bina Bangsa, Indonesia
(*) Corresponding Author

Abstract


Skin cancer is the most common type of cancer globally, and early detection is crucial for effective treatment. This research reviews the use of deep learning techniques in detecting and diagnosing skin cancer. A review of current methodologies was conducted to propose new strategies for improving the accuracy and reliability of the detection and diagnosis processes. Various deep learning models, including convolutional neural networks, were evaluated using three publicly available datasets. The PSO algorithm was utilized for segmentation and feature extraction, while also exploring the impact of transfer learning, data augmentation, and model ensemble on model accuracy. The findings of this study indicate that deep learning techniques can significantly enhance the detection and diagnosis of skin cancer

Full Text:

PDF

References


K. Urban, S. Mehrmal, P. Uppal, R. L. Giesey, and G. R. Delost, “The global burden of skin cancer: A longitudinal analysis from the Global Burden of Disease Study, 1990–2017,” JAAD Int., vol. 2, pp. 98–108, Mar. 2021, doi: 10.1016/j.jdin.2020.10.013.

E. R. Parker, “The influence of climate change on skin cancer incidence – A review of the evidence,” Int. J. Women’s Dermatology, vol. 7, no. 1, pp. 17–27, Jan. 2021, doi: 10.1016/j.ijwd.2020.07.003.

J. Shao, J. Feng, J. Li, S. Liang, W. Li, and C. Wang, “Novel tools for early diagnosis and precision treatment based on artificial intelligence,” Chinese Med. J. Pulm. Crit. Care Med., vol. 1, no. 3, pp. 148–160, Sep. 2023, doi: 10.1016/j.pccm.2023.05.001.

D. Painuli, S. Bhardwaj, and U. Köse, “Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review,” Comput. Biol. Med., vol. 146, p. 105580, Jul. 2022, doi: 10.1016/j.compbiomed.2022.105580.

I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 6, p. 420, Nov. 2021, doi: 10.1007/s42979-021-00815-1.

X. Chen et al., “Recent advances and clinical applications of deep learning in medical image analysis,” Med. Image Anal., vol. 79, p. 102444, Jul. 2022, doi: 10.1016/j.media.2022.102444.

L. Wang, “Deep Learning Techniques to Diagnose Lung Cancer,” Cancers (Basel)., vol. 14, no. 22, p. 5569, Nov. 2022, doi: 10.3390/cancers14225569.

K. A. Tran, O. Kondrashova, A. Bradley, E. D. Williams, J. V. Pearson, and N. Waddell, “Deep learning in cancer diagnosis, prognosis and treatment selection,” Genome Med., vol. 13, no. 1, p. 152, Dec. 2021, doi: 10.1186/s13073-021-00968-x.

A. Zadeh Shirazi, M. Tofighi, A. Gharavi, and G. A. Gomez, “The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide,” Technol. Cancer Res. Treat., vol. 23, Jan. 2024, doi: 10.1177/15330338241250324.

E. Rezk, M. Haggag, M. Eltorki, and W. El-Dakhakhni, “A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges,” Healthc. Anal., vol. 4, p. 100259, Dec. 2023, doi: 10.1016/j.health.2023.100259.

A. Shah et al., “A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN),” Clin. eHealth, vol. 6, pp. 76–84, Dec. 2023, doi: 10.1016/j.ceh.2023.08.002.

A. Takiddin, J. Schneider, Y. Yang, A. Abd-Alrazaq, and M. Househ, “Artificial Intelligence for Skin Cancer Detection: Scoping Review,” J. Med. Internet Res., vol. 23, no. 11, p. e22934, Nov. 2021, doi: 10.2196/22934.

D. Raval and J. N. Undavia, “A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images,” Healthc. Anal., vol. 3, p. 100199, Nov. 2023, doi: 10.1016/j.health.2023.100199.

S. Nazari and R. Garcia, “Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review,” Life, vol. 13, no. 11, p. 2123, Oct. 2023, doi: 10.3390/life13112123.

S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” Procedia Comput. Sci., vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.

M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Computation, vol. 11, no. 3, p. 52, Mar. 2023, doi: 10.3390/computation11030052.

J. Su, X. Yu, X. Wang, Z. Wang, and G. Chao, “Enhanced transfer learning with data augmentation,” Eng. Appl. Artif. Intell., vol. 129, p. 107602, Mar. 2024, doi: 10.1016/j.engappai.2023.107602.

B. Li, Y. Hou, and W. Che, “Data augmentation approaches in natural language processing: A survey,” AI Open, vol. 3, pp. 71–90, 2022, doi: 10.1016/j.aiopen.2022.03.001.

K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks,” Multimed. Tools Appl., vol. 79, no. 33–34, pp. 24029–24055, Sep. 2020, doi: 10.1007/s11042-020-09067-2.

A. Adegun and S. Viriri, “Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art,” Artif. Intell. Rev., vol. 54, no. 2, pp. 811–841, Feb. 2021, doi: 10.1007/s10462-020-09865-y.

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,” Mach. Learn. with Appl., vol. 5, p. 100036, Sep. 2021, doi: 10.1016/j.mlwa.2021.100036.

M. Dildar et al., “Skin Cancer Detection: A Review Using Deep Learning Techniques,” Int. J. Environ. Res. Public Health, vol. 18, no. 10, p. 5479, May 2021, doi: 10.3390/ijerph18105479.

K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neurosci. Informatics, vol. 2, no. 4, p. 100034, Dec. 2022, doi: 10.1016/j.neuri.2021.100034.

P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Sci. Data, vol. 5, no. 1, p. 180161, Aug. 2018, doi: 10.1038/sdata.2018.161.

M. Salvi, U. R. Acharya, F. Molinari, and K. M. Meiburger, “The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis,” Comput. Biol. Med., vol. 128, p. 104129, Jan. 2021, doi: 10.1016/j.compbiomed.2020.104129.

R. A. Lotufo, R. Audigier, A. V. Saúde, and R. C. Machado, “Morphological Image Processing,” in Microscope Image Processing, Elsevier, 2023, pp. 75–117. doi: 10.1016/B978-0-12-821049-9.00012-5.

X. Song and H. Li, “Segmentation Based on Particle Swarm Optimization,” 2021, pp. 731–736. doi: 10.1007/978-3-030-53980-1_107.

J. C. Canales, J. C. Canales, F. García-Lamont, A. Yee-Rendon, J. S. R. Castilla, and L. R. Mazahua, “Optimal segmentation of image datasets by genetic algorithms using color spaces,” Expert Syst. Appl., vol. 238, p. 121950, Mar. 2024, doi: 10.1016/j.eswa.2023.121950.

L. Xu, B. Song, and M. Cao, “An improved particle swarm optimization algorithm with adaptive weighted delay velocity,” Syst. Sci. Control Eng., vol. 9, no. 1, pp. 188–197, Jan. 2021, doi: 10.1080/21642583.2021.1891153.

F. Marini and B. Walczak, “Particle swarm optimization (PSO). A tutorial,” Chemom. Intell. Lab. Syst., vol. 149, pp. 153–165, Dec. 2015, doi: 10.1016/j.chemolab.2015.08.020.

S. Tijjani, M. N. Ab Wahab, and M. H. Mohd Noor, “An enhanced particle swarm optimization with position update for optimal feature selection,” Expert Syst. Appl., vol. 247, p. 123337, Aug. 2024, doi: 10.1016/j.eswa.2024.123337.

X. Zhang, “Convolutional Neural Networks and Architectures,” in Handbook of Face Recognition, Cham: Springer International Publishing, 2024, pp. 37–65. doi: 10.1007/978-3-031-43567-6_2.

I. Singh, G. Goyal, and A. Chandel, “AlexNet architecture based convolutional neural network for toxic comments classification,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 7547–7558, Oct. 2022, doi: 10.1016/j.jksuci.2022.06.007.

A. Deshpande, V. V. Estrela, and P. Patavardhan, “The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50,” Neurosci. Informatics, vol. 1, no. 4, p. 100013, Dec. 2021, doi: 10.1016/j.neuri.2021.100013.

M. B. Hossain, S. M. H. S. Iqbal, M. M. Islam, M. N. Akhtar, and I. H. Sarker, “Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images,” Informatics Med. Unlocked, vol. 30, p. 100916, 2022, doi: 10.1016/j.imu.2022.100916.

A. Alshammari, “Construction of VGG16 Convolution Neural Network (VGG16_CNN) Classifier with NestNet-Based Segmentation Paradigm for Brain Metastasis Classification,” Sensors, vol. 22, no. 20, p. 8076, Oct. 2022, doi: 10.3390/s22208076.

M. K. Monika, N. Arun Vignesh, C. Usha Kumari, M. N. V. S. S. Kumar, and E. L. Lydia, “Skin cancer detection and classification using machine learning,” Mater. Today Proc., vol. 33, pp. 4266–4270, 2020, doi: 10.1016/j.matpr.2020.07.366.

G. Algan and I. Ulusoy, “Image classification with deep learning in the presence of noisy labels: A survey,” Knowledge-Based Syst., vol. 215, p. 106771, Mar. 2021, doi: 10.1016/j.knosys.2021.106771.

I. D. Dinov, “Model Performance Assessment, Validation, and Improvement,” 2023, pp. 477–531. doi: 10.1007/978-3-031-17483-4_9.

G. Naidu, T. Zuva, and E. M. Sibanda, “A Review of Evaluation Metrics in Machine Learning Algorithms,” 2023, pp. 15–25. doi: 10.1007/978-3-031-35314-7_2.

B. Shetty, R. Fernandes, A. P. Rodrigues, R. Chengoden, S. Bhattacharya, and K. Lakshmanna, “Skin lesion classification of dermoscopic images using machine learning and convolutional neural network,” Sci. Rep., vol. 12, no. 1, p. 18134, Oct. 2022, doi: 10.1038/s41598-022-22644-9.

O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci. Rep., vol. 14, no. 1, p. 6086, Mar. 2024, doi: 10.1038/s41598-024-56706-x.




DOI: https://doi.org/10.30645/kesatria.v5i3.451

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.451.g446

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: