Abstract
The Invariant Dataset Augmentation (IDA) method shows its advantages in image classification using CNN networks. In the paper, several pretrained neural networks have been used e.g. Inception-ResNet-v2, VGG19, Xception, etc., trained on the PH2 and Derm7pt dermoscopic datasets in different scenarios. One of them relies on the Invariant Dataset Augmentation not only for training and but also for validation and testing. That original research and method show that the classification characteristics e.g. values of the weighted accuracy and sensitivity (true positive rate), and precision (positive predictive rate) tests F1 and MCC are much higher. That general approach provides better results with higher classification results e.g. sensitivity and precision (positive predictive rate) and can be used in other disciplines. In the paper, the results are shown in the research of the assessment of the skin lesions on two distinct dermoscopic datasets, PH2 and Derm7pt, on the classification of the presence or its absence of the feature called blue-white veil within the lesion.
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References
European Cancer Information System (ECIS). https://ecis.jrc.ec.europa.eu. Accessed 14 Jul 2021
ACS - American Cancer Society. https://www.cancer.org/research/cancer-facts-statistics.html. Accessed 14 Jul 2021
Milczarski, P., Beczkowski, M., Borowski, N.: Blue-white veil classification of dermoscopy images using convolutional neural networks and invariant dataset augmentation. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 226, pp. 421–432. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75075-6_34
Beczkowski, M., Borowski, N., Milczarski, P.: Classification of dermatological asymmetry of the skin lesions using pretrained convolutional neural networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2021. LNCS (LNAI), vol. 12855, pp. 3–14. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87897-9_1
Mendoncca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., Rozeira, J.: PH2 - a dermoscopic image database for research and benchmarking. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, pp. 5437–5440 (2013)
Argenziano, G., Soyer, H.P., De Giorgi, V., et al.: Interactive Atlas of Dermoscopy. EDRA Medical Publishing & New Media, Milan (2002)
Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE J. Biomed. Health Inform. 23(2), 538–546 (2019)
Soyer, H.P., Argenziano, G., Zalaudek, I., et al.: Three-point checklist of dermoscopy. A new screening method for early detection of melanoma. Dermatology 208(1), 27–31 (2004)
Argenziano, G., Soyer, H.P., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J. Am. Acad. Dermatol. 48(9), 679–693 (2003)
Milczarski, P.: Symmetry of hue distribution in the images. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10842, pp. 48–61. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91262-2_5
Argenziano, G., Fabbrocini, G., et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134, 1563–1570 (1998)
Nachbar, F., Stolz, W., Merkle, T., et al.: The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Am. Acad. Dermatol. 30(4), 551–559 (1994)
Milczarski, P., Stawska, Z., Maslanka, P.: Skin lesions dermatological shape asymmetry measures. In: Proceedings of the IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, pp. 1056–1062 (2017)
Menzies, S.W., Zalaudek, I.: Why perform dermoscopy? The evidence for its role in the routine management of pigmented skin lesions. Arch. Dermatol. 142, 1211–1222 (2006)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Conference Track Proceedings of 3rd International Conference on Learning Representations (ICRL), San Diego, USA, (2015)
Was, L., Milczarski, P., Stawska, Z., Wiak, S., Maslanka, P., Kot, M.: Verification of results in the acquiring knowledge process based on IBL methodology. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10841, pp. 750–760. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91253-0_69
Celebi, M.E., Kingravi, H.A., Uddin, B.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)
Was, L., et al.: Analysis of dermatoses using segmentation and color hue in reference to skin lesions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 677–689. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_61
Milczarski, P., Stawska, Z., Was, L., Wiak, S., Kot, M.: New dermatological asymmetry measure of skin lesions. Int. J. Neural Netw. Adv. Appl. 4, 32–38 (2017)
Milczarski, P., Stawska, Z.: Classification of skin lesions shape asymmetry using machine learning methods. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) WAINA 2020. AISC, vol. 1150, pp. 1274–1286. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44038-1_116
The International Skin Imaging Collaboration: Melanoma Project. http://isdis.net/isic-project/. Accessed 14 Jul 2021
Esteva, A., Kuprel, B., Novoa, R.A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
He, K., Zhang, X., Ren S. and Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Madooei, A., Drew, M.S., Sadeghi, M., Atkins, M.S.: Automatic detection of blue-white veil by discrete colour matching in dermoscopy images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 453–460. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40760-4_57
Jaworek-Korjakowska, J., Kłeczek, P., Grzegorzek, M., Shirahama, K.: Automatic detection of blue-whitish veil as the primary dermoscopic feature. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI and LNB), vol. 10245, pp. 649–657. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_58
Celebi, M.E., et al.: Automatic detection of blue-white veil and related structures in dermoscopy images. CMIG 32(8), 670–677 (2008)
Di Leo, G., Fabbrocini, G., Paolillo, A., Rescigno, O., Sommella, P.: Toward an automatic diagnosis system for skin lesions: estimation of blue-whitish veil and regression structures. In: International Multi-Conference on Systems, Signals & Devices, SSD 2009 (2009)
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Milczarski, P., Beczkowski, M., Borowski, N. (2021). Enhancing Dermoscopic Features Classification in Images Using Invariant Dataset Augmentation and Convolutional Neural Networks. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_34
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