Research Article
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Year 2023, Volume: 6 Issue: 2, 114 - 122, 31.08.2023

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

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

Abstract

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.

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Year 2023, Volume: 6 Issue: 2, 114 - 122, 31.08.2023

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

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

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

Convolutional Neural Network Transfer Learning Image Classification Skin Cancer

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.
There are 22 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Ahmet Furkan Sönmez 0000-0002-5678-2860

Serap Çakar 0000-0002-3682-0831

Feyza Cerezci 0000-0002-1596-1109

Muhammed Kotan 0000-0002-5218-8848

İbrahim Delibaşoğlu 0000-0001-8119-2873

Gülüzar Çit 0000-0002-1220-0558

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 2023Volume: 6 Issue: 2

Cite

IEEE 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, 2023, doi: 10.35377/saucis...1314638.
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