Artificial intelligence in the detection of melanomas
Keywords:
artificial intelligence, melanoma, skin cancer, neural networks, datasets
Abstract
Given that the field of artificial intelligence in the detection of melanoma has been opened, this research work is carried out with the aim of trying to contribute a grain of sand to this field, it is worth mentioning that melanoma skin cancer is one of the most more aggressive in terms of mortality and the best way to combat it is through its early detection. Five predictive models were implemented, four based on CNN convolutional neural networks, and one under the Vision Transformer ViT architecture, all these models were trained with datasets built from dermoscopic images obtained from the official website of the ISIC platform that promotes the implementation of artificial intelligence for the fight against skin cancer. The results obtained were not as expected, however, the possible causes are exposed and a line of future work is projected.
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References
[1]E. Mezquita, “Detección de melanomas con inteligencia artificial,” Diario Médico, p. 12, 13 Enero 2014.
[2]American Cancer Society, “¿Qué avances hay en las investigaciones sobre el cáncer de piel tipo melanoma?,” 14 Agosto 2019. [En línea]. Available: https://www.cancer.org/es/cancer/cancer-de-piel-tipo-melanoma/acerca/nuevas-investigaciones.html#escrito_por.
[3]C. Marín, G. H. Alférez, J. Córdova y V. González, “Detection of melanoma through image recognition and artificial neural networks,” IFMBE Proceedings, vol. 51, pp. 832-835, 7-12 Junio 2015.
[4]A. Adegun y S. Viriri, “Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art,” Artificial Intelligence Review, 27 Junio 2020.
[5]J. J. Rangel-Cortes, J. S. Ruiz-Castilla, F. García-Lamont y J. Cervantes-Canales, “Redes Neuronales Convolucionales en la identificación de melanomas benignos y malignos,” Revista Ibérica de Sistemas e Tecnologias de Informação, nº E23, pp. 15-27, Octubre 2019.
[6]S. Abhinav y J. Dheeba, “Convolutional Neural Network for Classifying Melanoma Images,” 2020.
[7]C. Yu, S. Yang, W. Kim, J. Jung, K.-Y. Chung, S. W. Lee y B. Oh, “Acral melanoma detection using a convolutional neural network for dermoscopy images,” PloS ONE, vol. 13, nº 3, p. e0193321, 24 Abril 2018.
[8]S. Nasiri, J. Helsper, M. Jung y M. Fathi, “DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images,” BMC Bioinformatics, vol. 21, nº 84, 11 Marzo 2020.
[9]M. S. S. Mahecha, O. J. S. Parra y J. B. Velandia, “Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks,” Challenges and Opportunities in the Digital Era, vol. 11195, pp. 605-616, 12 Octubre 2018.
[10]A. M. Alqudah, H. Alquraan y I. A. Qasmieh, “Segmented and non-segmented skin lesions classification using transfer learning and adaptative moment learning rate technique using pretrained convolutional neural network,” Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 42, pp. 67-78, 2019.
[11]H. El-Khatib, D. Popescu y L. Ichim, “Deep learning-based methods for automatic diagnosis of skin lesions,” Sensors, vol. 20, nº 6, p. 1753, 2020.
[12]J. A. Almaraz-Damian, V. Ponomaryov, S. Sadovnychiy y H. Castillejos-Fernandez, “Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures,” Entropy, vol. 22, nº 4, p. 484, 2020.
[13]Q. Yuan y S. Tavildar, “An open solution to ISIC 2018 classification and segmentation challenges,” 2018.
[14]D. Hao, J. Y. Seok, D. Ng, N. K. Yuan y F. M, “ISIC Challenge 2018,” 2018.
[15]M. Molina-Moreno, I. González-Díaz y F. Díaz-de-María, “An elliptical shape-regularized convolutional neural network for skin lesion segmentation,” 2018.
[16]S. Zhou, Y. Zhuang y R. Meng, “Multi-category skin lesion diagnosis using dermoscopy images and deep CNN ensembles,” 2019.
[17]A. G. C. Pachecoa, A. R. Alib y T. Trappenber, “Skin cancer detection based on deep learning andentropy to detect outlier samples,” 2019.
[18]V. Chouhan, “Skin lesion analysis towards melanoma detection with deep convolutional neural network,” 2019.
[19]T. Dat, D. T. Lan, T. T. H. Nguyen, T. T. N. Nguyen, H. P. Nguyen, L. Phuong y T. Z. Nguyen, “Ensembled skin cancer classification (ISIC 2019 challenge submission),” 2019.
[20]P. Zhang, “MelaNet: a deep dense attention network for melanoma detection in dermoscopy images,” 2019.
[21]J. Xing, C. Zeng, H. Yangwen, W. Tao, Y. Mao, S. Wang, Y. Zheng y R. Wang, “Open-set recognition of dermoscopic images with ensemble of deep convolutional networks,” 2019.
[22]Z. M. Yousef y H. Motahari, “Skin lesion analysis towards melanoma detection using softmax ensemble model and sigmoid ensemble model,” 2019.
[23]S. Cohen y N. Shimoni, “TTA meta learning for anomaly detection on skin lesion,” 2019.
[2]American Cancer Society, “¿Qué avances hay en las investigaciones sobre el cáncer de piel tipo melanoma?,” 14 Agosto 2019. [En línea]. Available: https://www.cancer.org/es/cancer/cancer-de-piel-tipo-melanoma/acerca/nuevas-investigaciones.html#escrito_por.
[3]C. Marín, G. H. Alférez, J. Córdova y V. González, “Detection of melanoma through image recognition and artificial neural networks,” IFMBE Proceedings, vol. 51, pp. 832-835, 7-12 Junio 2015.
[4]A. Adegun y S. Viriri, “Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art,” Artificial Intelligence Review, 27 Junio 2020.
[5]J. J. Rangel-Cortes, J. S. Ruiz-Castilla, F. García-Lamont y J. Cervantes-Canales, “Redes Neuronales Convolucionales en la identificación de melanomas benignos y malignos,” Revista Ibérica de Sistemas e Tecnologias de Informação, nº E23, pp. 15-27, Octubre 2019.
[6]S. Abhinav y J. Dheeba, “Convolutional Neural Network for Classifying Melanoma Images,” 2020.
[7]C. Yu, S. Yang, W. Kim, J. Jung, K.-Y. Chung, S. W. Lee y B. Oh, “Acral melanoma detection using a convolutional neural network for dermoscopy images,” PloS ONE, vol. 13, nº 3, p. e0193321, 24 Abril 2018.
[8]S. Nasiri, J. Helsper, M. Jung y M. Fathi, “DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images,” BMC Bioinformatics, vol. 21, nº 84, 11 Marzo 2020.
[9]M. S. S. Mahecha, O. J. S. Parra y J. B. Velandia, “Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks,” Challenges and Opportunities in the Digital Era, vol. 11195, pp. 605-616, 12 Octubre 2018.
[10]A. M. Alqudah, H. Alquraan y I. A. Qasmieh, “Segmented and non-segmented skin lesions classification using transfer learning and adaptative moment learning rate technique using pretrained convolutional neural network,” Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 42, pp. 67-78, 2019.
[11]H. El-Khatib, D. Popescu y L. Ichim, “Deep learning-based methods for automatic diagnosis of skin lesions,” Sensors, vol. 20, nº 6, p. 1753, 2020.
[12]J. A. Almaraz-Damian, V. Ponomaryov, S. Sadovnychiy y H. Castillejos-Fernandez, “Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures,” Entropy, vol. 22, nº 4, p. 484, 2020.
[13]Q. Yuan y S. Tavildar, “An open solution to ISIC 2018 classification and segmentation challenges,” 2018.
[14]D. Hao, J. Y. Seok, D. Ng, N. K. Yuan y F. M, “ISIC Challenge 2018,” 2018.
[15]M. Molina-Moreno, I. González-Díaz y F. Díaz-de-María, “An elliptical shape-regularized convolutional neural network for skin lesion segmentation,” 2018.
[16]S. Zhou, Y. Zhuang y R. Meng, “Multi-category skin lesion diagnosis using dermoscopy images and deep CNN ensembles,” 2019.
[17]A. G. C. Pachecoa, A. R. Alib y T. Trappenber, “Skin cancer detection based on deep learning andentropy to detect outlier samples,” 2019.
[18]V. Chouhan, “Skin lesion analysis towards melanoma detection with deep convolutional neural network,” 2019.
[19]T. Dat, D. T. Lan, T. T. H. Nguyen, T. T. N. Nguyen, H. P. Nguyen, L. Phuong y T. Z. Nguyen, “Ensembled skin cancer classification (ISIC 2019 challenge submission),” 2019.
[20]P. Zhang, “MelaNet: a deep dense attention network for melanoma detection in dermoscopy images,” 2019.
[21]J. Xing, C. Zeng, H. Yangwen, W. Tao, Y. Mao, S. Wang, Y. Zheng y R. Wang, “Open-set recognition of dermoscopic images with ensemble of deep convolutional networks,” 2019.
[22]Z. M. Yousef y H. Motahari, “Skin lesion analysis towards melanoma detection using softmax ensemble model and sigmoid ensemble model,” 2019.
[23]S. Cohen y N. Shimoni, “TTA meta learning for anomaly detection on skin lesion,” 2019.
Published
2022-06-15
Section
Artículos
