Research Article
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Classification of Melanoma Cancer Using Deep Convolutional Neural Networks

Year 2024, Volume: 10 Issue: 4, 996 - 1006, 31.12.2024
https://doi.org/10.28979/jarnas.1505804

Abstract

Accurate detection of skin diseases is crucial in healthcare, with early diagnosis being particularly vital for effective treatment. Melanoma, a form of skin cancer with a high potential for metastasis, requires early detection to significantly improve treatment success and prevent further spread across the skin. This study investigates the application of machine learning techniques to diagnose skin lesions, focusing on differentiating between benign moles and malignant melanoma. A Convolutional Neural Network (CNN) model was developed to explore machine learning's efficacy in this context. The initial model featured a primary architecture, progressively refined by adding additional layers and filters to increase its complexity. This iterative enhancement aimed to improve the model’s capability to extract and analyze features from skin images. Each model configuration was meticulously evaluated through a series of experiments to determine its diagnostic performance. The results revealed that the proposed CNN model achieved a high accuracy rate of 91\%. This significant finding demonstrates the effectiveness of machine learning approaches in the early diagnosis and management of melanoma. The study confirms that advanced CNN architectures can enhance diagnostic precision, thereby contributing to improved patient outcomes in detecting and treating skin diseases.

References

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  • K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in T. Tuytelaars, F.-F. Li, R. Bajcsy (Eds.), 29th IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016, pp. 770-778.
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  • M. Tan, Q. V. Le, EfficientNet: Rethinking model scaling for convolutional neural networks, in K. Chaudhuri, R. Salakhutdinov (Eds.), 36th International Conference on Machine Learning, California, 2019, pp. 6105-6114.
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  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. Chen, MobileNetV2: Inverted residuals and linear bottlenecks, in M. S. Brown, B. Morse, S. Peleg (Eds.), IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018, pp. 4510-4520.
  • S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8) (1997) 1735-1780.
  • J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks, in Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), 28th International Conference on Neural Information Processing Systems, Cambridge, 2014, pp. 3320-3328.
  • K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in Y. Bengio, Y. LeCun (Eds.), International Conference on Learning Representations, San Diego, 2015, pp. 1-14.
  • H. Bay, T. Tuytelaars, L. Van Gool, SURF: Speeded up robust features, in A. Leonardis, H. Bischof, A. Pinz (Eds.), 9th European Conference on Computer Vision, Graz, 2006, pp. 404-417.
  • D. G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60 (2) (2004) 91-110.
  • M. C. Mukkamala, M. Hein, Variants of RMSProp and Adagrad with Logarithmic Regret Bounds, in D. Precup, Y. W. Teh (Eds.), 34th International Conference on Machine Learning, Sydney, 2017, pp. 2545-2553.
  • C. M. Bishop, Pattern recognition and machine learning, Springer, New York, 2006.
  • E. Fix, J. L. Hodges, Discriminatory analysis, nonparametric discrimination: Consistency properties, Technical Report USAF School of Aviation Medicine (1951) Dayton.
  • T. K. Ho, Random decision forests, in C. Y. Suen (Ed.), 3rd International Conference on Document Analysis and Recognition, Montreal, 1995, pp. 278-282.
  • T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in B. Krishnapuram, M. Shah (Eds.), 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016, pp. 785-794.
  • Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell. Caffe: Convolutional architecture for fast feature embedding, in K. A. Hua, Y. Rui, R. Steinmetz (Eds.), 22nd ACM International Conference on Multimedia, Orlando, 2014, 675-678.
  • B. A. Olshausen, D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature 381 (1996) 607-609.
  • A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in F. Pereira, C. J. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, Lake Tahoe, 2012, pp. 1097-1105.
  • T. G. Dietterich, Ensemble methods in machine learning, in J. Kittler, F. Roli (Eds.), Multiple Classifier Systems, Cagliari, 2000, pp. 1-15.
  • M. Kearns, Thoughts on Hypothesis Boosting (1988), https://www.cis.upenn.edu/~mkearns/, Accessed 10 May 2024.
Year 2024, Volume: 10 Issue: 4, 996 - 1006, 31.12.2024
https://doi.org/10.28979/jarnas.1505804

Abstract

References

  • A. Masood, A. Al-Jumaily, Computer-aided diagnostic support system for skin cancer: A review of techniques and algorithms, International Journal of Biomedical Imaging 2013 (1) (2013) 323268 22 pages.
  • International Agency for Research on Cancer, https://www.iarc.who.int, Accessed on 15 May 2024.
  • American Cancer Society, Key Statistics for Melanoma Skin Cancer (2024), https://www.cancer.org/cancer/melanoma-skin-cancer/about/key-statistics.html, Accessed on 15 May 2024.
  • K. Thomsen, L. Iversen, T. L. Titlestad, O. Winther, Systematic review of machine learning for diagnosis and prognosis in dermatology, Journal of Dermatological Treatment 31 (5) (2020) 496-510.
  • J. R. Quinlan, Induction of decision trees, Machine Learning 1 (1986) 81-106.
  • C. Cortes, V. Vapnik, Support-vector networks, Machine Learning 20 (3) (1995) 273-297.
  • F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review 65 (6) (1958) 386–-408.
  • N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, J. R. Smith, Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images, in L. Zhou, L. Wang, Q. Wang, Y. Shi (Eds.), Machine Learning in Medical Imaging, 2015, pp. 118-126.
  • G. Litjens, C. I. Sanchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. H. van de Kaa, P. Bult, B. ban Ginneken, J. van der Laak, Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Scientific Reports 6 (2016) Article Number 26286 11 pages.
  • S. Liu, W. Cai, S. Pujol, R. Kikinis, D. Feng, Early diagnosis of Alzheimer's disease with deep learning, in G. Wang, B. He (Eds.), IEEE 11th International Symposium on Biomedical Imaging, Beijing, 2014, pp. 1015-1018.
  • G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Vercauteren, Interactive medical image segmentation using deep learning with image-specific fine-tuning, IEEE Transactions on Medical Imaging 37 (7) (2018) 1562-1573.
  • U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in Biology and Medicine 100 (2018) 270-278.
  • A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks, Nature 542 (2017) 115-118.
  • B. Harangi, Skin lesion classification with ensembles of deep convolutional neural networks, Journal of Biomedical Informatics 86 (2018) 25-32.
  • J. Yap, W. Yolland, P. Tschandl, Multimodal skin lesion classification using deep learning, Experimental Dermatology 27 (11) (2018) 1261-1267.
  • K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in T. Tuytelaars, F.-F. Li, R. Bajcsy (Eds.), 29th IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016, pp. 770-778.
  • N. Gessert, M. Nielsen, M. Shaikh, R. Werner, A. Schlaefer, Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data, MethodsX 7 (2020) 100864 8 pages.
  • M. Tan, Q. V. Le, EfficientNet: Rethinking model scaling for convolutional neural networks, in K. Chaudhuri, R. Salakhutdinov (Eds.), 36th International Conference on Machine Learning, California, 2019, pp. 6105-6114.
  • J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in M. S. Brown, B. Morse, S. Peleg (Eds.), IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018, pp. 7132-7141.
  • D. Mahajan, R. Girshick, V. Ramanathan, H. Kaiming, M. Paluri, Y. Li, A. Bharambe, L. V. D. Maaten, Exploring the limits of weakly supervised pretraining, in V. Ferrari, M. Hebert, C. Sminchisescu, Y. Weiss (Eds.), 15th European Conference on Computer Vision, Munich, 2018, pp. 181-196.
  • P. N. Srinivasu, J. G. SivaSai, M. F. Ijaz, A. K. Bhoi, W. Kim, J. J. Kang, Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM, Sensors 21 (8) (2021) 2852 27 pages.
  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. Chen, MobileNetV2: Inverted residuals and linear bottlenecks, in M. S. Brown, B. Morse, S. Peleg (Eds.), IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018, pp. 4510-4520.
  • S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8) (1997) 1735-1780.
  • J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks, in Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), 28th International Conference on Neural Information Processing Systems, Cambridge, 2014, pp. 3320-3328.
  • K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in Y. Bengio, Y. LeCun (Eds.), International Conference on Learning Representations, San Diego, 2015, pp. 1-14.
  • H. Bay, T. Tuytelaars, L. Van Gool, SURF: Speeded up robust features, in A. Leonardis, H. Bischof, A. Pinz (Eds.), 9th European Conference on Computer Vision, Graz, 2006, pp. 404-417.
  • D. G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60 (2) (2004) 91-110.
  • M. C. Mukkamala, M. Hein, Variants of RMSProp and Adagrad with Logarithmic Regret Bounds, in D. Precup, Y. W. Teh (Eds.), 34th International Conference on Machine Learning, Sydney, 2017, pp. 2545-2553.
  • C. M. Bishop, Pattern recognition and machine learning, Springer, New York, 2006.
  • E. Fix, J. L. Hodges, Discriminatory analysis, nonparametric discrimination: Consistency properties, Technical Report USAF School of Aviation Medicine (1951) Dayton.
  • T. K. Ho, Random decision forests, in C. Y. Suen (Ed.), 3rd International Conference on Document Analysis and Recognition, Montreal, 1995, pp. 278-282.
  • T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in B. Krishnapuram, M. Shah (Eds.), 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016, pp. 785-794.
  • Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell. Caffe: Convolutional architecture for fast feature embedding, in K. A. Hua, Y. Rui, R. Steinmetz (Eds.), 22nd ACM International Conference on Multimedia, Orlando, 2014, 675-678.
  • B. A. Olshausen, D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature 381 (1996) 607-609.
  • A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in F. Pereira, C. J. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, Lake Tahoe, 2012, pp. 1097-1105.
  • T. G. Dietterich, Ensemble methods in machine learning, in J. Kittler, F. Roli (Eds.), Multiple Classifier Systems, Cagliari, 2000, pp. 1-15.
  • M. Kearns, Thoughts on Hypothesis Boosting (1988), https://www.cis.upenn.edu/~mkearns/, Accessed 10 May 2024.
There are 37 citations in total.

Details

Primary Language English
Subjects Deep Learning
Journal Section Research Article
Authors

Ali Güneş 0000-0003-3116-1184

Emrah Dönmez 0000-0003-3345-8344

Publication Date December 31, 2024
Submission Date June 27, 2024
Acceptance Date October 16, 2024
Published in Issue Year 2024 Volume: 10 Issue: 4

Cite

APA Güneş, A., & Dönmez, E. (2024). Classification of Melanoma Cancer Using Deep Convolutional Neural Networks. Journal of Advanced Research in Natural and Applied Sciences, 10(4), 996-1006. https://doi.org/10.28979/jarnas.1505804
AMA Güneş A, Dönmez E. Classification of Melanoma Cancer Using Deep Convolutional Neural Networks. JARNAS. December 2024;10(4):996-1006. doi:10.28979/jarnas.1505804
Chicago Güneş, Ali, and Emrah Dönmez. “Classification of Melanoma Cancer Using Deep Convolutional Neural Networks”. Journal of Advanced Research in Natural and Applied Sciences 10, no. 4 (December 2024): 996-1006. https://doi.org/10.28979/jarnas.1505804.
EndNote Güneş A, Dönmez E (December 1, 2024) Classification of Melanoma Cancer Using Deep Convolutional Neural Networks. Journal of Advanced Research in Natural and Applied Sciences 10 4 996–1006.
IEEE A. Güneş and E. Dönmez, “Classification of Melanoma Cancer Using Deep Convolutional Neural Networks”, JARNAS, vol. 10, no. 4, pp. 996–1006, 2024, doi: 10.28979/jarnas.1505804.
ISNAD Güneş, Ali - Dönmez, Emrah. “Classification of Melanoma Cancer Using Deep Convolutional Neural Networks”. Journal of Advanced Research in Natural and Applied Sciences 10/4 (December 2024), 996-1006. https://doi.org/10.28979/jarnas.1505804.
JAMA Güneş A, Dönmez E. Classification of Melanoma Cancer Using Deep Convolutional Neural Networks. JARNAS. 2024;10:996–1006.
MLA Güneş, Ali and Emrah Dönmez. “Classification of Melanoma Cancer Using Deep Convolutional Neural Networks”. Journal of Advanced Research in Natural and Applied Sciences, vol. 10, no. 4, 2024, pp. 996-1006, doi:10.28979/jarnas.1505804.
Vancouver Güneş A, Dönmez E. Classification of Melanoma Cancer Using Deep Convolutional Neural Networks. JARNAS. 2024;10(4):996-1006.


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