Comparative Analysis of Convolutional Neural Networks for the Detection of Malignant Melanoma in Cutaneous Imagery

About this article

Submitted: Jun 23, 2023
Final Revised: Oct 23, 23
Accepted: Nov 20, 2023
Published: Dec 30, 2023
Abstract view: 21
PDF Download: 1
Volumes: Vol. 1 No. 2 (2023) Pages 99-112
DOI: 10.63208/21018-98

Full Text

License

Cite this article

How to Cite

[1]

W. Zeyu, L. H. Yen, A. Sultanov, and X. Meiling, “Comparative Analysis of Convolutional Neural Networks for the Detection of Malignant Melanoma in Cutaneous Imagery”, CAI, vol. 1, no. 2, pp. 99–112, Dec. 2023, doi: 10.63208/21018-98.

Abstract

Malignant melanoma represents a highly aggressive form of skin cancer associated with significant mortality rates in the absence of early detection. Consequently, the establishment of precise diagnostic protocols is imperative. Conventional diagnostic methods, which rely heavily on microscopic examination and the clinical expertise of dermatologists, are often labor-intensive and subject to interpretation variability. To address these limitations and enhance diagnostic precision, Artificial Intelligence (AI) has emerged as a valuable adjunctive tool for clinicians. This study investigates the efficacy of Deep Learning (DL) methodologies for melanoma detection utilizing cutaneous image processing. A comparative analysis was conducted across a spectrum of Convolutional Neural Network (CNN) architectures, specifically DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet. All models were trained and evaluated on a graphical processing unit (GPU) environment using a dataset comprising 7,146 images. The empirical results demonstrate that the GoogleNet architecture yielded the superior performance, achieving accuracies of 74.91% on the training set and 76.08% on the testing set.

Keywords: Malignant Melanoma Deep Learning Convolutional Neural Networks (CNN) Cutaneous Image Processing GoogleNet

References

Argenziano, G.; Longo, C.; Cameron, A.; Cavicchini, S.; Gourhant, J.Y.; Lallas, A.; McColl, I.; Rosendahl, C.; Thomas, L.; Tiodorovic-Zivkovic, D.; et al. Blue-black rule: A simple dermoscopic clue to recognize pigmented nodular melanoma. Br. J. Dermatol. 2011, 165, 1251–1255.

Vestergaard, M.; Macaskill, P.; Holt, P.; Menzies, S. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting. Br. J. Dermatol. 2008, 159, 669–676.

Charoenpong, J.; Pimpunchat, B.; Amornsamankul, S.; Triampo, W.; Nuttavut, N. A Comparison of Machine Learning Algorithms and their Applications. Int. J. Simul.–Syst. Sci. Technol. 2019, 20, 1–17.

Rezayi, S.; Mohammadzadeh, N.; Bouraghi, H.; Saeedi, S.; Mohammadpour, A. Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods. Comput. Intell. Neurosci. 2021, 2021, 5478157.

Tufail, A.B.; Ma, Y.K.; Kaabar, M.K.; Martínez, F.; Junejo, A.; Ullah, I.; Khan, R. Deep learning in cancer diagnosis and prognosis prediction: A minireview on challenges, recent trends, and future directions. Comput. Math. Methods Med. 2021, 2021, 9025470.

Li, X.; Jiao, H.; Wang, Y. Edge detection algorithm of cancer image based on deep learning. Bioengineered 2020, 11, 693–707.

Gheisari, M.; Wang, G.; Bhuiyan, M.Z.A. A survey on deep learning in big data. In Proceedings of the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, China, 21–24 July 2017; Volume 2, pp. 173–180.

Banka, A.A.; Mir, R.N. Current Big Data Issues and Their Solutions via Deep Learning: An Overview. Iraqi J. Electr. Electron. Eng. 2018, 14, 127–138.

Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M.A.; Al-Amidie, M.; Farhan, L. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021, 8, 1–74.

Chen, X.; Weng, J.; Lu, W.; Xu, J.; Weng, J. Deep manifold learning combined with convolutional neural networks for action recognition. IEEE Trans. Neural Netw. Learn. Syst. 2017, 29, 3938–3952.

Ghods, A.; Cook, D.J. A survey of deep network techniques all classifiers can adopt. Data Min. Knowl. Discov. 2021, 35, 46–87.

Liu, W.; Wang, Z.; Liu, X.; Zeng, N.; Liu, Y.; Alsaadi, F.E. A survey of deep neural network architectures and their applications. Neurocomputing 2017, 234, 11–26.

Yamashita, R.; Nishio, M.; Do, R.K.G.; Togashi, K. Convolutional neural networks: An overview and application in radiology. Insights Imaging 2018, 9, 611–629.

Khan, A.; Sohail, A.; Zahoora, U.; Qureshi, A.S. A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 2020, 53, 5455–5516.

Dong, Y.N.; Liang, G.S. Research and discussion on image recognition and classification algorithm based on deep learning. In Proceedings of the 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 8–10 November 2019; pp. 274–278.

Sharma, O. Deep challenges associated with deep learning. In Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 14–16 February 2019; pp. 72–75.

Rajput, D.S.; Reddy, T.S.K.; Raju, D.N. Investigation on Deep Learning Approach for Big Data: Applications and Challenges. In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2020; pp. 1016–1029.

Jan, B.; Farman, H.; Khan, M.; Imran, M.; Islam, I.U.; Ahmad, A.; Ali, S.; Jeon, G. Deep learning in big data analytics: A comparative study. Comput. Electr. Eng. 2019, 75, 275–287.

Zhang, Q.; Yang, L.T.; Chen, Z.; Li, P. A survey on deep learning for big data. Inf. Fusion 2018, 42, 146–157.

Qian, Y. Exploration of machine algorithms based on deep learning model and feature extraction. Math. Biosci. Eng. 2021, 18, 7602–7618.

Pouyanfar, S.; Sadiq, S.; Yan, Y.; Tian, H.; Tao, Y.; Reyes, M.P.; Shyu, M.L.; Chen, S.C.; Iyengar, S.S. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 2018, 51, 1–36.

Cullell-Dalmau, M.; Otero-Viñas, M.; Manzo, C. Research techniques made simple: Deep learning for the classification of dermatological images. J. Investig. Dermatol. 2020, 140, 507–514.

Schadendorf, D.; van Akkooi, A.C.; Berking, C.; Griewank, K.G.; Gutzmer, R.; Hauschild, A.; Stang, A.; Roesch, A.; Ugurel, S. Melanoma. Lancet 2018, 392, 971–984.

Chassagnon, G.; Vakalopolou, M.; Paragios, N.; Revel, M.P. Deep learning: Definition and perspectives for thoracic imaging. Eur. Radiol. 2020, 30, 2021–2030.

Hekler, A.; Utikal, J.S.; Enk, A.H.; Berking, C.; Klode, J.; Schadendorf, D.; Jansen, P.; Franklin, C.; Holland-Letz, T.; Krahl, D.; et al. Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur. J. Cancer 2019, 115, 79–83.

Acosta, M.F.J.; Tovar, L.Y.C.; Garcia-Zapirain, M.B.; Percybrooks, W.S. Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Med. Imaging 2021, 21, 6.

Brinker, T.J.; Hekler, A.; Enk, A.H.; Berking, C.; Haferkamp, S.; Hauschild, A.; Weichenthal, M.; Klode, J.; Schadendorf, D.; Holland-Letz, T.; et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur. J. Cancer 2019, 119, 11–17.

Bisla, D.; Choromanska, A.; Berman, R.S.; Stein, J.A.; Polsky, D. Towards automated melanoma detection with deep learning: Data purification and augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, 16–20 June 2019.

Abbas, Q.; Celebi, M.E. DermoDeep-A classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network. Multimed. Tools Appl. 2019, 78, 23559–23580.

Tognetti, L.; Bonechi, S.; Andreini, P.; Bianchini, M.; Scarselli, F.; Cevenini, G.; Moscarella, E.; Farnetani, F.; Longo, C.; Lallas, A.; et al. A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi. J. Dermatol. Sci. 2020, 101, 115–122.

Pennig, L.; Shahzad, R.; Caldeira, L.; Lennartz, S.; Thiele, F.; Goertz, L.; Zopfs, D.; Meißner, A.K.; Fürtjes, G.; Perkuhn, M.; et al. Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model. Am. J. Neuroradiol. 2021, 42, 655–662.

Zhou, Q.; Shi, Y.; Xu, Z.; Qu, R.; Xu, G. Classifying melanoma skin lesions using convolutional spiking neural networks with unsupervised stdp learning rule. IEEE Access 2020, 8, 101309–101319.

Pollastri, F.; Bolelli, F.; Paredes, R.; Grana, C. Augmenting data with GANs to segment melanoma skin lesions. Multimed. Tools Appl. 2020, 79, 15575–15592.

Winkler, J.K.; Fink, C.; Toberer, F.; Enk, A.; Deinlein, T.; Hofmann-Wellenhof, R.; Thomas, L.; Lallas, A.; Blum, A.; Stolz, W.; et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 2019, 155, 1135–1141.

Almaraz-Damian, J.A.; Ponomaryov, V.; Sadovnychiy, S.; Castillejos-Fernandez, H. Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy 2020, 22, 484.

Combalia, M.; Codella, N.C.; Rotemberg, V.; Helba, B.; Vilaplana, V.; Reiter, O.; Carrera, C.; Barreiro, A.; Halpern, A.C.; Puig, S.; et al. BCN20000: Dermoscopic lesions in the wild. arXiv 2019, arXiv:1908.02288.

Tschandl, P.; Rosendahl, C.; Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 2018, 5, 180161.

Codella, N.C.; Gutman, D.; Celebi, M.E.; Helba, B.; Marchetti, M.A.; Dusza, S.W.; Kalloo, A.; Liopyris, K.; Mishra, N.; Kittler, H.; 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 Proceedings of the 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), Washington, DC, USA, 4–7 April 2018; pp. 168–172.

Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.

Ragab, D.A.; Attallah, O.; Sharkas, M.; Ren, J.; Marshall, S. A framework for breast cancer classification using multi-DCNNs. Comput. Biol. Med. 2021, 131, 104245.

Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 1251–1258.

Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708.

He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.

He, K.; Zhang, X.; Ren, S.; Sun, J. Identity mappings in deep residual networks. In Computer Vision—ECCV 2016, Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 11–14 October 2016; Springer: Cham, Switzerland, 2016; pp. 630–645.

Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv:1704.04861.

Najafabadi, M.M.; Villanustre, F.; Khoshgoftaar, T.M.; Seliya, N.; Wald, R.; Muharemagic, E. Deep learning applications and challenges in big data analytics. J. Big Data 2015, 2, 1–21.

Christin, S.; Hervet, É.; Lecomte, N. Applications for deep learning in ecology. Methods Ecol. Evol. 2019, 10, 1632–1644.

Angermueller, C.; Pärnamaa, T.; Parts, L.; Stegle, O. Deep learning for computational biology. Mol. Syst. Biol. 2016, 12, 878.

Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; Fergus, R. Intriguing properties of neural networks. arXiv 2013, arXiv:1312.6199.

Khan, S.; Muhammad, K.; Mumtaz, S.; Baik, S.W.; de Albuquerque, V.H.C. Energy-efficient deep CNN for smoke detection in foggy IoT environment. IEEE Internet Things J. 2019, 6, 9237–9245.

Turki, T.; Wei, Z. Improved Deep Convolutional Neural Networks via Boosting for Predicting the Quality of In Vitro Bovine Embryos. Electronics 2022, 11, 1363.

Computing and Algorithm Insight

Open Access