Abstract
Skin cancer is a highly relevant health problem around the world. The World Health Organization (WHO) reports that one-third of the diagnosed cancers are skin cancer. It is well known that early detection of skin cancer significantly increases the prognosis of patients. In many cases, however, the absence of clinical devices and qualified experts makes this task very difficult. To overcome this problem, in the last years advanced deep learning techniques have been proposed to recognize skin cancer automatically showing promising results. In this work, we evaluate 50 deep learning approaches on the well-known HAM10000 dataset of dermatoscopic images from seven different skin lesion categories. The approaches have been evaluated using the same experimental protocol. In our experiments, the performance of each approach in terms of accuracy and computational time are measured. In addition, the number of trainable parameters and the number of features of the last layer of the deep learning architecture are given. Thus, comparison between the approaches can be easily established. The results showed that the best performance has been achieved by a deep learning approach based on visual transformers with 84.29% of accuracy on the testing subset. We know that these results may vary significantly on other datasets. For this reason, rather than establish which method is the best, the main contribution of this work is to make the 50 deep learning approaches available in a simple way for future research in the area. We believe that this methodology, i.e., training, testing and evaluating many deep learning methods, can be very helpful to establish which is the best architecture and what is the highest performance that can be achieved on new datasets.
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Notes
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The reader is referred to the review on convolutional neural networks in [8].
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It has been originally proposed in 1989 for handwritten digit recognition. ConvNet’s became more popular in the 2010s s with the introduction of inexpensive hardware such as GPUs (graphics processing units).
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See http://pytorch.org.
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References
Al-masni, M.A., Al-antari, M.A., Choi, M.T., Han, S.M., Kim, T.S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Programs Biomed. 162, 221–231 (2018)
Ashique, K., Kaliyadan, F., Aurangabadkar, S.: Clinical photography in dermatology using smartphones: an overview. Indian Dermatol. Online J. 6(3), 158 (2015)
Attia, M., Hossny, M., Zhou, H., Nahavandi, S., Asadi, H., Yazdabadi, A.: Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture. Comput. Methods Programs Biomed. 177, 17–30 (2019)
Attia, M., Hossny, M., Zhou, H., Nahavandi, S., Asadi, H., Yazdabadi, A.: Realistic hair simulator for skin lesion images: a novel benchemarking tool. Artif. Intell. Med. 108, 101933 (2020)
Badrinarayanan, V., Handa, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv preprint arXiv:1505.07293 (2015)
Barata, C., Celebi, M.E., Marques, J.S.: Improving dermoscopy image classification using color constancy. IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015)
Barata, C., Ruela, M., Francisco, M., Mendonca, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3), 965–979 (2014)
Bhatt, D., et al.: CNN variants for computer vision: history, architecture, application, challenges and future scope. Electronics 10(20), 2470 (2021)
Binder, M., Steiner, A., Schwarz, M., Knollmayer, S., Wolff, K., Pehamberger, H.: Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: a pilot study. Br. J. Dermatol. 130(4), 460–465 (1994)
Bissoto, A., Fornaciali, M., Valle, E., Avila, S.: (De) constructing bias on skin lesion datasets. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 2766–2774, June 2019
Brinker, T.J., et al.: Deep neural networks are superior to dermatologists in melanoma image classification. Eur. J. Cancer 119, 11–17 (2019)
Daneshjou, R.: Toward augmented intelligence: The first prospective, randomized clinical trial assessing clinician and artificial intelligence collaboration in dermatology. J. Invest. Dermatol. 142(9), 2301–2302 (2022)
Deda, L.C., Goldberg, R.H., Jamerson, T.A., Lee, I., Tejasvi, T.: Dermoscopy practice guidelines for use in telemedicine. NPJ Digit. Med. 5(1), 1–7 (2022)
Dosovitskiy, A., et al.: An image is worth \(16\times 16\) words: transformers for image recognition at scale. CoRR abs/2010.11929 (2020). https://arxiv.org/abs/2010.11929
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Ferrante di Ruffano, L., et al.: Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults (2018)
Ganster, H., Pinz, A., Röhrer, R., Wildling, E., Binder, M., Kittler, H.: Automated melanoma recognition. IEEE Trans. Med. Imaging 20(3), 233–239 (2001)
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J.: A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857 (2017)
Goyal, M., Knackstedt, T., Yan, S., Hassanpour, S.: Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities. Comput. Biol. Med. 127, 104065 (2020)
Hamblin, M.R., Avci, P., Gupta, G.K.: Imaging in Dermatology. Academic Press (2016)
Han, S.S., et al.: Evaluation of artificial intelligence-assisted diagnosis of skin neoplasms-a single-center, paralleled, unmasked, randomized controlled trial. J. Invest. Dermatol. 142(9), 2353–2362 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)
Heibel, H.D., Hooey, L., Cockerell, C.J.: A review of noninvasive techniques for skin cancer detection in dermatology. Am. J. Clin. Dermatol. 21, 513–524 (2020). https://doi.org/10.1007/s40257-020-00517-z
Howard, A., et al.: Searching for MobileNetV3. CoRR abs/1905.02244 (2019)
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016)
Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)1MB model size. CoRR abs/1602.07360 (2016)
Johr, R.H.: Dermoscopy: alternative melanocytic algorithms - the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist. Clin. Dermatol. 20(3), 240–247 (2002)
Kamińska-Winciorek, G., Placek, W.: The most common mistakes on dermatoscopy of melanocytic lesions. Postepy Dermatologii I Alergologii 32(1), 33–39 (2015)
Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks. CoRR abs/1404.5997 (2014)
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256. IEEE (2010)
Lee, I., Du, X., Anthony, B.: Hair segmentation using adaptive threshold from edge and branch length measures. Comput. Biol. Med. 89, 314–324 (2017)
Liu, Y., et al.: A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26(6), 900–908 (2020)
Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. CoRR abs/2201.03545 (2022). https://arxiv.org/abs/2201.03545
Ma, N., Zhang, X., Zheng, H., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. CoRR abs/1807.11164 (2018)
Moreira, D., Alves, P., Veiga, F., Rosado, L., Vasconcelos, M.J.M.: Automated mobile image acquisition of macroscopic dermatological lesions. In: HEALTHINF 2021–14th International Conference on Health Informatics; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021, pp. 122–132 (2021)
Muñoz-López, C., et al.: Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study. J. Eur. Acad. Dermatol. Venereol. 35(2), 546–553 (2021)
Navarrete-Dechent, C., Dusza, S.W., Liopyris, K., Marghoob, A.A., Halpern, A.C., Marchetti, M.A.: Automated dermatological diagnosis: hype or reality? J. Invest. Dermatol. 138(10), 2277 (2018)
Pacheco, A.G., Krohling, R.A.: The impact of patient clinical information on automated skin cancer detection. Comput. Biol. Med. 116, 103545 (2020)
Pacheco, A.G., et al.: PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief 32, 106221 (2020)
Reiter, O., Rotemberg, V., Kose, K., Halpern, A.C.: Artificial intelligence in skin cancer. Curr. Derm. Rep. 8, 133–140 (2019). https://doi.org/10.1007/s13671-019-00267-0
Reshma, G., et al.: Deep learning-based skin lesion diagnosis model using dermoscopic images. Intell. Autom. Soft Comput. 31(1), 621–634 (2022)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation. CoRR abs/1801.04381 (2018)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2016)
Sidoroff, A.: The role of clinical photography in dermatology. In: Imaging in Dermatology, pp. 5–11. Elsevier (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. CoRR abs/1409.4842 (2014)
Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820–2828 (2019)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. CoRR abs/1905.11946 (2019)
Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 1–9 (2018)
Vestergaard, M.E., Macaskill, P., Holt, P.E., Menzies, S.W.: 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. 159(3), 669–676 (2008)
Xie, S., Girshick, R.B., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. CoRR abs/1611.05431 (2016)
Yacin Sikkandar, M., Alrasheadi, B.A., Prakash, N.B., Hemalakshmi, G.R., Mohanarathinam, A., Shankar, K.: Deep learning based an automated skin lesion segmentation and intelligent classification model. J. Ambient. Intell. Humaniz. Comput. 12(3), 3245–3255 (2021). https://doi.org/10.1007/s12652-020-02537-3
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
Zakhem, G.A., Fakhoury, J.W., Motosko, C.C., Ho, R.S.: Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer. J. Am. Acad. Dermatol. 85(6), 1544–1556 (2021)
Zhang, J., Xie, Y., Xia, Y., Shen, C.: Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging 38(9), 2092–2103 (2019)
Acknowledgments
This work was supported by National Center for Artificial Intelligence CENIA FB210017, Basal ANID and ANID - iHealth, Millennium Science Initiative Program ICN2021_004.
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Mery, D. et al. (2023). On Skin Lesion Recognition Using Deep Learning: 50 Ways to Choose Your Model. In: Wang, H., et al. Image and Video Technology. PSIVT 2022. Lecture Notes in Computer Science, vol 13763. Springer, Cham. https://doi.org/10.1007/978-3-031-26431-3_9
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