Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Ahmet Furkan Sönmez; Serap Çakar; Feyza Cerezci; Muhammed Kotan; İbrahim Delibaşoğlu; Gülüzar Çit

doi:10.35377/saucis...1314638

EN

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Abstract

Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches the highest accuracy rate was obtained using the MobileNetV2 model with a rate of 80.79% accuracy.

Keywords

References

[1] A. R. Ali, J. Li, and S. J. O’Shea, "Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images," Plos one, 2020.
[2] H. Alquran, I. A. Qasmieh, A. M. Alqudah, S. Alhammouri, E. Alawneh, A. Abughazaleh, and F. Hasayen, "The melanoma skin cancer detection and classification using support vector machine," 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE, pp. 1-5, 2017.
[3] K. Pai, and A. Giridharan, "Convolutional Neural Networks for classifying skin lesions," TENCON 2019-2019 IEEE Region 10 Conference (TENCON), IEEE, pp. 1794-1796, 2019.
[4] I. K. E. Purnama, et al., "Disease classification based on dermoscopic skin images using convolutional neural network in teledermatology system," 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). IEEE, pp. 1-5, 2019.
[5] H. Gupta, H. Bhatia, D. Giri, R. Saxena, and R. Singh, "Comparison and Analysis of Skin Lesion on Pretrained Architectures," International Research Journal of Engineering and Technology (IRJET), pp. 2704-2707, 2020.
[6] X. Cao, J. S. Pan, Z. Wang, Z. Sun, A. Haq, W. Deng, and S. Yang, "Application of generated mask method based on Mask R-CNN in classification and detection of melanoma," Computer Methods and Programs in Biomedicine, vol. 207, 2021.
[7] M. A. Khan, T. Akram, Y. D. Zhang, and M. Sharif, "Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework," Pattern Recognition Letters, vol. 143, pp. 58-66, 2021.
[8] M. A. Khan, Y. D. Zhang, M. Sharif, and T. Akram, "Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification," Computers & Electrical Engineering, vol. 90, 2021.

[9] V. Srivastava, D. Kumar, and S. Roy, "A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer," Computers and Electrical Engineering, vol. 102, 2022.
[10] T. M. Alam, K. Shaukat, W. A. Khan, I. A. Hameed, L. A. Almuqren, M. A. Raza, M. Aslam, and S. Luo, "An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset," Diagnostics, vol. 12, no. 9, pp. 2115-2131, 2022.
[11] A. Bassel, A. B. Abdulkareem, Z. A. A. Alyasseri, N. S. Sani, and H. J. Mohammed, "Automatic malignant and benign skin cancer classification using a hybrid deep learning approach," Diagnostics, vol. 12, no. 10, 2022.
[12] W. Salma and A. S. Eltrass, "Automated deep learning approach for classification of malignant melanoma and benign skin lesions," Multimedia Tools and Applications, vol. 81, no. 22, pp. 32643-32660, 2022.
[13] 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," Scientific Reports, vol. 12, 2022.
[14] S. Aladhadh, M. Alsanea, M. Aloraini, T. Khan, S. Habib, and M. Islam, "An effective skin cancer classification mechanism via medical vision transformer," Sensors, vol. 22, no. 11, 2022.
[15] S. Iqbal, A. N. Qureshi, and G. Mustafa, "Hybridization of CNN with LBP for Classification of Melanoma Images," Computers, Materials & Continua, vol. 71, no. 3, 2022.
[16] I. A. Ahmed, E. M. Senan, H. S. A. Shatnawi, Z. M. Alkhraisha, M. M. A. Al-Azzam, "Multi-Models of Analyzing Dermoscopy Images for Early Detection of Multi-Class Skin Lesions Based on Fused Features," Processes, vol. 11, no. 3, 2023.
[17] G. Alwakid, W. Gouda, M. Humayun, NZ. Jhanjhi, "Diagnosing Melanomas in Dermoscopy Images Using Deep Learning," Diagnostics, vol. 13, no. 10, 2023.
[18] A. W. Setiawan, "Effect of Color Enhancement on Early Detection of Skin Cancer using Convolutional Neural Network," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pp. 100-103, IEEE, 2020.
[19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, 2012.
[20] P. Tschandl, C. Rosendahl, and H. Kittler, "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions," Scientific Data, vol. 5, no. 1, pp.1-9, 2018.
[21] C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of big data, vol. 6, no. 1, pp. 1-48, 2019.
[22] A. Mikołajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem," 2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, pp. 117-122, 2018.

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Ahmet Furkan Sönmez ^*
0000-0002-5678-2860
Türkiye

Serap Çakar
0000-0002-3682-0831
Türkiye

Feyza Cerezci
0000-0002-1596-1109
Türkiye

Muhammed Kotan
0000-0002-5218-8848
Türkiye

İbrahim Delibaşoğlu
0000-0001-8119-2873
Türkiye

Gülüzar Çit
0000-0002-1220-0558
Türkiye

Early Pub Date

August 27, 2023

Publication Date

August 31, 2023

Submission Date

June 14, 2023

Acceptance Date

July 26, 2023

Published in Issue

Year 1970 Volume: 6 Number: 2

DOI

https://doi.org/10.35377/saucis...1314638

IZ

https://izlik.org/JA27ME93GC

Cite

RIS / Bibtex

APA

Sönmez, A. F., Çakar, S., Cerezci, F., Kotan, M., Delibaşoğlu, İ., & Çit, G. (2023). Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions. Sakarya University Journal of Computer and Information Sciences, 6(2), 114-122. https://doi.org/10.35377/saucis...1314638

AMA

1.Sönmez AF, Çakar S, Cerezci F, Kotan M, Delibaşoğlu İ, Çit G. Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions. SAUCIS. 2023;6(2):114-122. doi:10.35377/saucis.1314638

Chicago

Sönmez, Ahmet Furkan, Serap Çakar, Feyza Cerezci, Muhammed Kotan, İbrahim Delibaşoğlu, and Gülüzar Çit. 2023. “Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions”. Sakarya University Journal of Computer and Information Sciences 6 (2): 114-22. https://doi.org/10.35377/saucis. 1314638.

EndNote

Sönmez AF, Çakar S, Cerezci F, Kotan M, Delibaşoğlu İ, Çit G (August 1, 2023) Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions. Sakarya University Journal of Computer and Information Sciences 6 2 114–122.

IEEE

[1]A. F. Sönmez, S. Çakar, F. Cerezci, M. Kotan, İ. Delibaşoğlu, and G. Çit, “Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions”, SAUCIS, vol. 6, no. 2, pp. 114–122, Aug. 2023, doi: 10.35377/saucis...1314638.

ISNAD

Sönmez, Ahmet Furkan - Çakar, Serap - Cerezci, Feyza - Kotan, Muhammed - Delibaşoğlu, İbrahim - Çit, Gülüzar. “Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions”. Sakarya University Journal of Computer and Information Sciences 6/2 (August 1, 2023): 114-122. https://doi.org/10.35377/saucis. 1314638.

JAMA

1.Sönmez AF, Çakar S, Cerezci F, Kotan M, Delibaşoğlu İ, Çit G. Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions. SAUCIS. 2023;6:114–122.

MLA

Sönmez, Ahmet Furkan, et al. “Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 2, Aug. 2023, pp. 114-22, doi:10.35377/saucis. 1314638.

Vancouver

1.Ahmet Furkan Sönmez, Serap Çakar, Feyza Cerezci, Muhammed Kotan, İbrahim Delibaşoğlu, Gülüzar Çit. Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions. SAUCIS. 2023 Aug. 1;6(2):114-22. doi:10.35377/saucis. 1314638

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Öz

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Cited By

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Leveraging Deep Learning for Accurate Classification of Skin Disease Images

Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection

In-Depth Review of Leveraging Deep Learning Advancements for Enhanced Skin Cancer Detection and Classification