Segmentation of Skin Lesions in Dermoscopic Images Using a Combination of Wavelet Transform and Modified U-Net Architecture

Fooladi, S.; Farsi, H.; Mohamadzadeh, S.

doi:10.22061/jecei.2024.10807.736

Articles in Press

Document Type : Original Research Paper

Authors

Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

https://doi.org/10.22061/jecei.2024.10807.736

Abstract

Background and Objectives: The increasing prevalence of skin cancer highlights the urgency for early intervention, emphasizing the need for advanced diagnostic tools. Computer-assisted diagnosis (CAD) offers a promising avenue to streamline skin cancer screening and alleviate associated costs.
Methods: This study endeavors to develop an automatic segmentation system employing deep neural networks, seamlessly integrating data manipulation into the learning process. Utilizing an encoder-decoder architecture rooted in U-Net and augmented by wavelet transform, our methodology facilitates the generation of high-resolution feature maps, thus bolstering the precision of the deep learning model.
Results: Performance evaluation metrics including sensitivity, accuracy, dice coefficient, and Jaccard similarity confirm the superior efficacy of our model compared to conventional methodologies. The results showed a accuracy of %96.89 for skin lesions in PH2 Database and %95.8 accuracy for ISIC 2017 database findings, which offers promising results compared to the results of other studies. Additionally, this research shows significant improvements in three metrics: sensitivity, Dice, and Jaccard. For the PH database, the values are 96, 96.40, and 95.40, respectively. For the ISIC database, the values are 92.85, 96.32, and 95.24, respectively.
Conclusion: In image processing and analysis, numerous solutions have emerged to aid dermatologists in their diagnostic endeavors The proposed algorithm was evaluated using two PH datasets, and the results were compared to recent studies. Impressively, the proposed algorithm demonstrated superior performance in terms of accuracy, sensitivity, Dice coefficient, and Jaccard Similarity scores when evaluated on the same database images compared to other methods.

Keywords

Main Subjects

Image Processing

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Publisher’s Note

JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher

Shahid Rajaee Teacher Training University

References

[1] A. Rehman, M. A. Butt, M. Zaman, "Attention Res-UNet: Attention residual UNet with focal Tversky loss for skin lesion segmentation," Int. J. Decis. Support Syst. Technol., 15(1): 1-17, 2023.

[2] M. K. Hasan, M. A. Ahamad, C. H. Yap, G. Yang, "A survey, review, and future trends of skin lesion segmentation and classification," Comput. Bio. Med., 106624, 2023.

[3] I. Ul Haq, J. Amin, M. Sharif, M. Almas Anjum, "Skin lesion detection using recent machine learning approaches," in Prognostic Models in Healthcare: AI and Statistical Approaches, pp. 193-211, Springer, 2022.

[4] S. Khattar, R. Kaur, "Computer assisted diagnosis of skin cancer: A survey and future recommendations," Comput. Electr. Eng., 104: 108431, 2022.

[5] S. Bakheet, S. Alsubai, A. El-Nagar, A. Alqahtani, "A multi-feature fusion framework for automatic skin cancer diagnostics," Diagnostics, 13(8): 1474, 2023.

[6] S. Fooladi, H. Farsi, S. Mohamadzadeh, "Segmenting the lesion area of brain tumor using convolutional neural networks and fuzzy k-means clustering," Int. J. Eng. Trans. B: Appl., 36(8): 1556-1568, 2023.

[7] N. Ul Huda, R. Amin, S. I. Gillani, M. Hussain, A. Ahmed, H. Aldabbas, "Skin cancer malignancy classification and segmentation using machine learning algorithms," JOM: 1-15, 2023.

[8] Y. Wu, B. Chen, A. Zeng, D. Pan, R. Wang, S. Zhao, "Skin cancer classification with deep learning: a systematic review," Front. Oncol., 12: 893972, 2022.

[9] S. Fooladi, H. Farsi, S. Mohamadzadeh, "Detection and classification of skin cancer using deep learning," J. Birjand Univ. Med. Sci., 26( 1): 44-53, 2019.

[10] S. Fooladi, H. Farsi, "Segmentation of cancer cell in histopathologic images of breast cancer and lesion area in skin cancer images using convolutional neural networks," Med. J. Tabriz Univ. Med. Sci. Health Serv., 42(5): 520-528, 2020.

[11] F. Afza, M. Sharif, M. Mittal, M. A. Khan, D. J. Hemanth, "A hierarchical three-step superpixels and deep learning framework for skin lesion classification," Methods, 202: 88-102, 2022.

[12] M. A. Khan, Y. D. Zhang, M. Sharif, T. Akram, "Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification," Comput. Electr. Eng., 90: 106956, 2021.

[13] M. A. Khan, T. Akram, Y.-D. Zhang, M. Sharif, "Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework," Pattern Recognit. Lett., 143: 58-66, 2021.

[14] M. Z. Alom, T. Aspiras, T. M. Taha, V. K. Asari, "Skin cancer segmentation and classification with improved deep convolutional neural network," in Proc. Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 11318: 291-301, SPIE, 2020.

[15] G. Nasreen, K. Haneef, M. Tamoor, A. Irshad, "A comparative study of state-of-the-art skin image segmentation techniques with CNN," Multimedia Tools Appl., 82(7): 10921-10942, 2023.

[16] M. Nasir, M. A. Khan, M. Sharif, I. U. Lali, T. Saba, T. Iqbal, "An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach," Microsc. Res. Tech., 81(6): 528-543, 2018.

[17] E. İ. Ünlü, A. Çinar, "Segmentation of benign and malignant lesions on skin images using U-Net," in Proc. 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT): 165-169, 2021.

[18] K. Zafar et al., "Skin lesion segmentation from dermoscopic images using convolutional neural network," Sensors, 20(6): 1601, 2020.

[19] M. Naqvi, S. Q. Gilani, T. Syed, O. Marques, H. C. Kim, "Skin cancer detection using deep learning-A review," Diagnostics, 13(11): 1911, 2023.

[20] M. Asadi-Aghbolaghi, R. Azad, M. Fathy, S. Escalera, "Multi-level context gating of embedded collective knowledge for medical image segmentation," arXiv preprint arXiv:2003.05056, 2020.

[21] M. D. Alahmadi, "Multiscale attention U-Net for skin lesion segmentation," IEEE Access, 10: 59145-59154, 2022.

[22] M. A. Al-Masni, M. A. Al-Antari, M. T. Choi, S. M. Han, T. S. Kim, "Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks," Comput. Methods Programs Biomed., 162: 221-231, 2018.

[23] C. Zhao, R. Shuai, L. Ma, W. Liu, M. Wu, "Segmentation of skin lesions image based on U-Net++," Multimedia Tools Appl., 81(6): 8691-8717, 2022.

[24] S. Öztürk, U. Özkaya, "Skin lesion segmentation with improved convolutional neural network," J. Digital Imaging, 33: 958-970, 2020.

[25] L. Liu, Y. Y. Tsui, M. Mandal, "Skin lesion segmentation using deep learning with auxiliary task," J. Imaging, 7(4): 67, 2021.

[26] H. Wang, J. Yang, "FBUNet: Full convolutional network based on fusion block architecture for biomedical image segmentation," J. Med. Biol. Eng., 41: 185-202, 2021.

[27] S. Mohamadzadeh, S. Pasban, J. Zeraatkar-Moghadam, A. K. Shafiei, "Parkinson’s disease detection by using feature selection and sparse representation," J. Med. Biol. Eng., 41(4): 412-421, 2021.

[28] F. Yu, V. Koltun, "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv: 1511.07122, 2015.

[29] H. K. Gajera, D. R. Nayak, M. A. Zaveri, "A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features," Biomed. Signal Process. Control, 79: 104186, 2023.

[30] B. Cassidy, C. Kendrick, A. Brodzicki, J. Jaworek-Korjakowska, M. H. Yap, "Analysis of the ISIC image datasets: Usage, benchmarks and recommendations," Med. Image Anal., 75: 102305, 2022.

[31] K. M. Hosny, D. Elshora, E. R. Mohamed, E. Vrochidou, G. A. Papakostas, "Deep learning and optimization-based methods for skin lesions segmentation: A review," IEEE Access, 11: 85467- 85488, 2023.

[32] L. Bi, J. Kim, E. Ahn, D. Feng, "Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks," arXiv preprint arXiv:1703.04197, 2017.

[33] Z. Yuan, "Automatic skin lesion segmentation with fully convolutional-deconvolutional networks," arXiv preprint arXiv:1703.05165, 2017.

[34] J. Zhu, Z. Liu, W. Gao, Y. Fu, "CotepRes-Net: An efficient U-Net based deep learning method of liver segmentation from computed tomography images," Biomed. Signal Process. Control, 88: 105660, 2024.

[35] M. Niazi, K. Rahbar, "Entropy kernel graph cut feature space enhancement with squeezenet deep neural network for textural image segmentation," Int. J. Image Graphics, 24: 2550064, 2024.

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Electrical and Computer Engineering Innovations (JECEI)

Segmentation of Skin Lesions in Dermoscopic Images Using a Combination of Wavelet Transform and Modified U-Net Architecture

References

References

Send comment about this article

Articles in Press, Accepted Manuscript
Available Online from 04 November 2024

Segmentation of Skin Lesions in Dermoscopic Images Using a Combination of Wavelet Transform and Modified U-Net Architecture

References

References

Send comment about this article

Articles in Press, Accepted Manuscript Available Online from 04 November 2024

Articles in Press, Accepted Manuscript
Available Online from 04 November 2024