پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 709 |
| تعداد مقالات | 10,213 |
| تعداد مشاهده مقاله | 71,680,611 |
| تعداد دریافت فایل اصل مقاله | 63,498,576 |
تولید داده های جدید ملانوما با استفاده از ترکیب شبکه متخاصم مولد و الگوی باینری محلی | ||
| مدل سازی در مهندسی | ||
| دوره 23، شماره 80، فروردین 1404، صفحه 147-158 اصل مقاله (714 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.31553.2515 | ||
| نویسندگان | ||
| ویدا اسماعیلی؛ محمود محصل فقهی* | ||
| دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران | ||
| تاریخ دریافت: 29 مرداد 1402، تاریخ بازنگری: 25 فروردین 1403، تاریخ پذیرش: 24 تیر 1403 | ||
| چکیده | ||
| پایگاه دادههای درموسکوپیِ در دسترسِ سرطان پوست ملانوما دارای تصاویر کم و نامتوازن با روشنایی غیریکنواخت هستند که روشهای شناسایی ملانوما را با چالش مواجه کردهاند. برای حل این مشکلات، در این مقاله، روش جدیدی را برای تولید دادههای جدید شامل ملانوما پیشنهاد شده است. در واقع، روش جدیدِ پیشنهادی، شبکه متخاصم مولد را با الگوی باینری محلی ترکیب میکند. به عبارت دیگر، ابتدا تصاویر موجود در پایگاه داده برای آموزش وارد شبکهی متخاصم مولد میشوند. سپس، تصاویر جدید فراوانی تولید میشود و در نهایت، روش الگوی باینری محلی به آنها اعمال میشود. بنابراین، تعداد دادههای جدید تولید شده زیاد و متعادل است و دادههای تولید شده، تغییرات روشنایی ندارند. همچنین، این دادهها ویژگیهای مفید و معناداری را نشان میدهند که تمایز بین ملانوما و خال را بیشتر میسازد. آزمایشهای انجام شده نشان داده است که روش پیشنهاد تأثیر خوبی در افزایش دقت شناسایی ملانوما دارد. طبق نتایج روش پیشنهادی عملکرد شبکه عصبی کانولوشنی را %7 افزایش داده است. | ||
| کلیدواژهها | ||
| الگوی باینری محلی؛ تولید دادههای جدید؛ سرطان پوست؛ شبکه متخاصم مولد؛ ملانوما | ||
| عنوان مقاله [English] | ||
| Generating New Melanoma Data Using a Combination of Generative Adversarial Network and Local Binary Pattern | ||
| نویسندگان [English] | ||
| Vida Esmaeili؛ Mahmood Mohassel Feghhi | ||
| Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| The available melanoma skin cancer dermoscopy databases have low and unbalanced images with non-uniform illumination that make melanoma detection methods challenging. To address these problems, in this paper, we propose a new method to generate new data including melanoma. In fact, our new proposed method combines generative adversarial network and local binary pattern. On the other hand, first, the images existing in the dataset are fed to the generative adversarial network. Then, many new images are generated and finally, the local binary pattern is applied to them. Therefore, the number of the new generated data is large and balanced and the generated data does not have illumination changes. Also, these data show useful and meaningful features that increase the difference between melanoma and nevi. The experiments have shown that the proposed method has a good effect on increasing the accuracy of melanoma diagnosis. According to the results, the proposed method has increased the convolutional neural network's efficiency by 7%. | ||
| کلیدواژهها [English] | ||
| Local binary pattern, Generating new data, Skin cancer, Generative adversarial network, Melanoma | ||
| مراجع | ||
|
[1] W .Salma, and A.S. Eltrass. "Automated deep learning approach for classification of malignant melanoma and benign skin lesions." Multimedia Tools and Applications 81, no. 22 (2022): 32643-32660. [2] G. Alwakid, W. Gouda, M. Humayun, and N. Us Sama. "Melanoma detection using deep learning-based classifications." In Healthcare, vol. 10, no. 12, p. 2481. MDPI, 2022. [3] A. Budhiman, S. Suyanto, and A. Arifianto. "Melanoma cancer classification using resnet with data augmentation." 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 17-20. IEEE, 2019. [4] M.A. Kassem, K.M. Hosny, R. Damaševičius, and M. Meselhy Eltoukhy. "Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review." Diagnostics 11, no. 8 (2021): 1390. [5] A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, and S. Thrun. "Dermatologist-level classification of skin cancer with deep neural networks." Nature 542, no. 7639 (2017): 115-118. [6] S. Kundu, A. Panja, and S. Karforma. "Detection of Melanoma Skin Cancer Using Hybrid Machine Learning Techniques." Science and Culture 89, (2023): 70-76. [7] S. Fooladi, H. Farsi, and S. Mohamadzadeh. "Detection and classification of skin cancer using deep learning." J Birjand Univ Med Sci 26, no. 1 (2019): 44-53. [8] J.N. Nwafor, B.E. Torere, E. Agu, L. Kadiku, T. Ogunyemi, P.A. Akinsanya, O.O. Araromi, D.E. Akahara, and O.E. Okobi. "The Role of Biomarkers in the Diagnosis and Prognosis of Different Stages of Melanoma." Cureus 15, no. 5 (2023). [9] H. Mhaske, M. Patil, J. Thote, A. Shendage, and R. Tallapalli. "A review on melanoma cancer detection using artificial intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023). [10] Z. Turani, E. Fatemizadeh, T. Blumetti, S. Daveluy, A. Flavia Moraes, W. Chen, D. Mehregan, P.E. Andersen, and M. Nasiriavanaki. "Optical radiomic signatures derived from optical coherence tomography images improve identification of melanoma." Cancer Research 79, no. 8 (2019): 2021-2030. [11] A.L. Kotian, K.J. Madhura, and P.T. Rahul. "Machine Learning-Based Melanoma Skin Cancer Detection." International Journal of Engineering Management and Humanities 4, no. 3 (2023): 72-76. [12] I. Obuchowska, and J. Konopińska. "Importance of optical coherence tomography and optical coherence tomography angiography in the imaging and differentiation of choroidal melanoma: a review." Cancers 14, no. 14 (2022): 3354. [13] D. Jeba Derwin, O. Jeba Singh, B. Priestly Shan, K. Uma Maheswari, and D. Lavanya. "An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in Melanoma detection." Medical & Biological Engineering & Computing 61, no. 11 (2023): 2921-2938. [14] S. Kumar, G. Jaiswal, and K. Sinha. Skin Cancer Lesion Detection Using Improved CNN Techniques. Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0. IGI Global. pp. 355-377. 2023. [15] R. Kaur, H. GholamHosseini, R. Sinha, and M. Lindén. "Melanoma classification using a novel deep convolutional neural network with dermoscopic images." Sensors 22, no. 3 (2022): 1134. [16] T. Mendonça, P.M. Ferreira, J.S. Marques, A.R. Marcal, and J. Rozeira. "PH 2-A dermoscopic image database for research and benchmarking." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5437-5440. IEEE, 2013. [17] D. Gutman, N.C. Codella, E. Celebi, B. Helba, M. Marchetti, N. Mishra, and A. Halpern. "Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC)." arXiv preprint arXiv:1605.01397 (2016). [18] N.C. Codella, D. Gutman, M. Emre Celebi, B. Helba, M.A. Marchetti, S.W. Dusza, A. Kalloo et al. "Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)." In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168-172. IEEE, 2018. [19] NCF. Codella et al." ISIC 2018: Skin Lesion Analysis towards Melanoma Detection." Available online: https://challenge2018.isic-archive.com/ (accessed on 9 May 2021). [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 5, no. 1 (2018): 1-9. [21] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. "Generative adversarial nets." Advances in Neural Information Processing Systems 27 (2014). [22] T. Ojala, K. Valkealahti, E. Oja, and M. Pietikäinen. "Texture discrimination with multidimensional distributions of signed gray-level differences." Pattern Recognition 34, no. 3 (2001): 727-739. [23] M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen. Computer vision using local binary patterns. Vol. 40. Springer Science & Business Media, 2011. [24] C. Nicholson. "Evaluation metrics for machine learning—accuracy, precision, recall, and F1 defined", ed: Pathmind. http://pathmind.com/wiki/accuracyprecision-recall-f1, 2019. [25] Q. Su, H. Nuzly Abdull Hamed, M. Adham Isa, X. Hao, and X. Dai. "A GAN-based data augmentation method for imbalanced multi-class skin lesion classification." IEEE Access (2024). [26] M.A. Rasel, U.H. Obaidellah, and S. Abdul Kareem. "convolutional neural network-based skin lesion classification with Variable Nonlinear Activation Functions." IEEE Access 10, (2022): 83398-83414. [27] F.W. Alsaade, T.H.H. Aldhyani, and M.H. Al-Adhaileh. "Developing a recognition system for diagnosing melanoma skin lesions using artificial intelligence algorithms." Computational and Mathematical Methods in Medicine 2021, no. 1 (2021): 1-20. [28] X. Li, J. Wu, E.Z. Chen, and H. Jiang. "From deep learning towards finding skin lesion biomarkers." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2797-2800. IEEE, 2019. | ||
|
آمار تعداد مشاهده مقاله: 448 تعداد دریافت فایل اصل مقاله: 323 |
||