(Universitas Gadjah Mada, Indonesia)(2) Igi Ardiyanto
(Universitas Gadjah Mada, Indonesia)(3) * Hanung Adi Nugroho
(Universitas Gadjah Mada, Indonesia)*corresponding author
AbstractThe occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance.
KeywordsPigmented Skin Lesion; Dermoscopy; Melanoma; Skin Cancer; Convolutional Neural Network
|
DOIhttps://doi.org/10.26555/ijain.v9i3.961 |
Article metricsAbstract views : 878 | PDF views : 249 |
Cite |
Full Text Download
|
References
[1] A. Hon and A. Oakley, “Pigmented skin lesions,” DermNet NZ, 2015. Available at: https://dermnetnz.org/topics/pigmented-skin-lesions
[2] S. H. Kassani and P. H. Kassani, “A comparative study of deep learning architectures on melanoma detection,” Tissue Cell, vol. 58, no. April, pp. 76–83, 2019, doi: 10.1016/j.tice.2019.04.009.
[3] A. M. Alqudah, H. Alquran, and I. A. Qasmieh, “Segmented and non-segmented skin lesions classification using transfer learning and adaptive moment learning rate technique using pretrained convolutional neural network,” J. Biomimetics, Biomater. Biomed. Eng., vol. 42, pp. 67–78, 2019, doi: 10.4028/www.scientific.net/JBBBE.42.67.
[4] M. Cullell-Dalmau, M. Otero-Viñas, and C. Manzo, “Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images,” J. Invest. Dermatol., vol. 140, no. 3, pp. 507–514, Mar. 2020, doi: 10.1016/j.jid.2019.12.029.
[5] A. Jibhakate, P. Parnerkar, S. Mondal, V. Bharambe, and S. Mantri, “Skin Lesion Classification using Deep Learning and Image Processing,” in 3rd International Conference on Intelligent Sustainable Systems (ICISS), IEEE, Dec. 2020, pp. 333–340. doi: 10.1109/ICISS49785.2020.9316092.
[6] E. Okur and M. Turkan, “A survey on automated melanoma detection,” Eng. Appl. Artif. Intell., vol. 73, no. March, pp. 50–67, Aug. 2018, doi: 10.1016/j.engappai.2018.04.028.
[7] WHO, “Skin Cancers,” 2019. Available at: https://www.iarc.who.int/cancer-type/skin-cancer/.
[8] S. A. Margolis, “Skin cancer medicine integral to Australian general practice,” Aust. J. Gen. Pract., vol. 48, no. 6, pp. 343–343, Jun. 2019, doi: 10.31128/AJGP-06-19-1234e.
[9] R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2019,” CA. Cancer J. Clin., vol. 69, no. 1, pp. 7–34, Jan. 2019, doi: 10.3322/caac.21551.
[10] M. E. Celebi, N. Codella, and A. Halpern, “Dermoscopy Image Analysis: Overview and Future Directions,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 2, pp. 474–478, 2019, doi: 10.1109/JBHI.2019.2895803.
[11] M. H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, and S. M. R. Soroushmehr, “Skin lesion segmentation in clinical images using deep learning,” in 23rd International Conference on Pattern Recognition (ICPR), IEEE, Dec. 2016, pp. 337–342. doi: 10.1109/ICPR.2016.7899656.
[12] S. Pathan, K. G. G. Prabhu, and P. C. C. Siddalingaswamy, “Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review,” Biomed. Signal Process. Control, vol. 39, pp. 237–262, 2018, doi: 10.1016/j.bspc.2017.07.010.
[13] R. B. Oliveira, J. P. Papa, A. S. Pereira, and J. M. R. S. Tavares, “Computational methods for pigmented skin lesion classification in images: review and future trends,” Neural Comput. Appl., vol. 29, no. 3, pp. 613–636, 2018, doi: 10.1007/s00521-016-2482-6.
[14] T. J. Brinker, A. Hekler, J. S. Utikal, N. Grabe, D. Schadendorf, and J. Klode, “Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review,” J. Med. Internet Res., vol. 20, no. 10, pp. 1–8, Oct. 2018, doi: 10.2196/11936.
[15] A. Adegun and S. Viriri, “Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art,” Artif. Intell. Rev., vol. 54, no. 2, pp. 811–841, Feb. 2021, doi: 10.1007/s10462-020-09865-y.
[16] B. Kitchenham, Procedures for Performing Systematic Reviews, Version 1.1. Keele University Technical Report TR/SE-0401, 2005. Available at: https://www.inf.ufsc.br/~aldo.vw/kitchenham.pdf
[17] J. Jesson, L. Matheson, and F. M. Lacey, Doing your systematic review - Traditional and systematic techniques. SAGE Publications Ltd, 2011. Available at: https://www.worldcat.org/title/706789667
[18] R. S. Wahono, “A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks,” J. Softw. Eng., vol. 1, no. 1, pp. 1–12, Jan. 2015. Available at: https://media.neliti.com/media/publications/90270-EN-a-systematic-literature-review-of-softwa
[19] B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” EBSE Technical Report Version 2.3 - University of Durham, 2007. Available at: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide
[20] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
[21] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in a conference paper at ICLR 2015, pp. 1–14, 2014. doi: 10.48550/arXiv.1409.1556.
[22] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-December, pp. 770–778, Dec. 2016, doi: 10.1109/CVPR.2016.90.
[23] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-December, pp. 2818–2826, Dec. 2016, doi: 10.1109/CVPR.2016.308.
[24] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 2261–2269, Nov. 2017, doi: 10.1109/CVPR.2017.243.
[25] A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” pp. 1-9, doi: 10.48550/arXiv.1704.04861.
[26] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, pp. 10691–10700, 2019. doi: 10.48550/arXiv.1905.11946.
[27] H. Cho, Y. Kim, E. Lee, D. Choi, Y. Lee, and W. Rhee, “Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks,” IEEE Access, vol. 8, pp. 52588–52608, 2020, doi: 10.1109/ACCESS.2020.2981072.
[28] K. Swersky, J. Snoek, and R. P. Adams, “Multi-task Bayesian optimization,” Adv. Neural Inf. Process. Syst., pp. 1–9, 2013. Available at: http://papers.neurips.cc/paper/5086-multi-task-bayesian-optimization.pdf.
[29] S. Nasiri, J. Helsper, M. Jung, and M. Fathi, “DePicT Melanoma Deep-CLASS: A deep convolutional neural networks approach to classify skin lesion images,” BMC Bioinformatics, vol. 21, pp. 1–13, 2020, doi: 10.1186/s12859-020-3351-y.
[30] M. A. Albahar, “Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer,” IEEE Access, vol. 7, pp. 38306–38313, 2019, doi: 10.1109/ACCESS.2019.2906241.
[31] Q. Abbas and M. E. Celebi, “DermoDeep-A classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network,” Multimed. Tools Appl., vol. 78, no. 16, pp. 23559–23580, 2019, doi: 10.1007/s11042-019-7652-y.
[32] K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Classification of skin lesions using transfer learning and augmentation with Alex-net,” PLoS One, vol. 14, no. 5, pp. 1-17 May 2019, doi: 10.1371/journal.pone.0217293.
[33] N. Hameed, A. M. Shabut, M. K. Ghosh, and M. A. Hossain, “Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques,” Expert Syst. Appl., vol. 141, pp. 1–18, Mar. 2020, doi: 10.1016/j.eswa.2019.112961.
[34] H. A. Haenssle et al., “Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions,” Ann. Oncol., vol. 31, no. 1, pp. 137–143, Jan. 2020, doi: 10.1016/j.annonc.2019.10.013.
[35] J. Kawahara, S. Daneshvar, G. Argenziano, and G. Hamarneh, “Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 2, pp. 538–546, Mar. 2019, doi: 10.1109/JBHI.2018.2824327.
[36] J. Yap, W. Yolland, and P. Tschandl, “Multimodal skin lesion classification using deep learning,” Exp. Dermatol., vol. 27, no. 11, pp. 1261–1267, Nov. 2018, doi: 10.1111/exd.13777.
[37] P. Tang, Q. Liang, X. Yan, S. Xiang, and D. Zhang, “GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 10, pp. 2870–2882, Oct. 2020, doi: 10.1109/JBHI.2020.2977013.
[38] A. Mahbod, G. Schaefer, I. Ellinger, R. Ecker, A. Pitiot, and C. Wang, “Fusing fine-tuned deep features for skin lesion classification,” Comput. Med. Imaging Graph., vol. 71, pp. 19–29, Jan. 2019, doi: 10.1016/j.compmedimag.2018.10.007.
[39] C. Barata and J. S. Marques, “Deep Learning For Skin Cancer Diagnosis With Hierarchical Architectures,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, Apr. 2019, pp. 841–845. doi: 10.1109/ISBI.2019.8759561.
[40] B. Harangi, “Skin lesion classification with ensembles of deep convolutional neural networks,” J. Biomed. Inform., vol. 86, no. August, pp. 25–32, Oct. 2018, doi: 10.1016/j.jbi.2018.08.006.
[41] J. Yang, F. Xie, H. Fan, Z. Jiang, and J. Liu, “Classification for Dermoscopy Images Using Convolutional Neural Networks Based on Region Average Pooling,” IEEE Access, vol. 6, pp. 65130–65138, 2018, doi: 10.1109/ACCESS.2018.2877587.
[42] J. Zhang, Y. Xie, Y. Xia, and C. Shen, “Attention Residual Learning for Skin Lesion Classification,” IEEE Trans. Med. Imaging, vol. 38, no. 9, pp. 2092–2103, Sep. 2019, doi: 10.1109/TMI.2019.2893944.
[43] Y. Yan, J. Kawahara, and G. Hamarneh, “Melanoma Recognition via Visual Attention,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, 2019, pp. 793–804. doi: 10.1007/978-3-030-20351-1_62.
[44] I. González-Díaz, “DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 2, pp. 547–559, 2019, doi: 10.1109/JBHI.2018.2806962.
[45] N. C. F. Codella, C.-C. Lin, A. Halpern, M. Hind, R. Feris, and J. R. Smith, “Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images,” in Understanding and Interpreting Machine Learning in Medical Image Computing Applications, , vol. 11038 LNCS, pp. 97–105, Springer, 2018. doi: 10.1007/978-3-030-02628-8_11.
[46] P. Tschandl, G. Argenziano, M. Razmara, and J. Yap, “Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features,” Br. J. Dermatol., vol. 181, no. 1, pp. 155–165, 2019, doi: 10.1111/bjd.17189.
[47] W. M. Salamaa and M. H. Aly, “Deep learning design for benign and malignant classification of skin lesions: a new approach,” Multimed. Tools Appl., vol. 80, no. 17, pp. 26795–26811, Jul. 2021, doi: 10.1007/s11042-021-11000-0.
[48] U. A. Usmani, J. Watada, J. Jaafar, I. A. Aziz, and A. Roy, “A reinforcement learning algorithm for automated detection of skin lesions,” Appl. Sci., vol. 11, no. 20, pp.9367, 2021, doi: 10.3390/app11209367.
[49] J. Song, J. Li, S. Ma, J. Tang, and F. Guo, “Melanoma Classification in Dermoscopy Images via Ensemble Learning on Deep Neural Network,” in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, pp. 751–756. doi: 10.1109/BIBM49941.2020.9313451.
[50] Z. Wei, F. Shi, L. Chen, Q. Li, and H. Song, “Multi-level contexts aggregation for melanoma recognition under feature confusion regularization,” Signal, Image Video Process., vol. 16, no. 2, pp. 411–418, Mar. 2022, doi: 10.1007/s11760-021-01949-8.
[51] A. Mahbod, G. Schaefer, C. Wang, G. Dorffner, R. Ecker, and I. Ellinger, “Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification,” Comput. Methods Programs Biomed., vol. 193, pp. 1–9, 2020, doi: 10.1016/j.cmpb.2020.105475.
[52] J.-A. Almaraz-Damian, V. Ponomaryov, S. Sadovnychiy, and 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, Apr. 2020, doi: 10.3390/e22040484.
[53] B. Harangi, A. Baran, and A. Hajdu, “Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support,” Biomed. Signal Process. Control, vol. 62, p. 102041, 2020, doi: 10.1016/j.bspc.2020.102041.
[54] C. Barata, J. S. Marques, and M. E. Celebi, “Deep Attention Model for the Hierarchical Diagnosis of Skin Lesions,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2757-2765, 2019. doi: 10.1109/CVPRW.2019.00334.
[55] J. Wu, W. Hu, Y. Wang, and Y. Wen, “A Multi-Input CNNs with Attention for Skin Lesion Classification,” in 2020 IEEE International Conference on Smart Cloud (SmartCloud), IEEE, Nov. 2020, pp. 78–83. doi: 10.1109/SmartCloud49737.2020.00023.
[56] N. Gessert et al., “Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting,” IEEE Trans. Biomed. Eng., vol. 67, no. 2, pp. 495–503, 2020, doi: 10.1109/TBME.2019.2915839.
[57] R. C. Maron et al., “Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks,” Eur. J. Cancer, vol. 119, pp. 57–65, Sep. 2019, doi: 10.1016/j.ejca.2019.06.013.
[58] K. Thurnhofer-Hemsi, E. Lopez-Rubio, E. Dominguez, and D. A. Elizondo, “Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting,” IEEE Access, vol. 9, pp. 112193–112205, 2021, doi: 10.1109/ACCESS.2021.3103410.
[59] A. A. Adegun and S. Viriri, “FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images,” IEEE Access, vol. 8, pp. 150377–150396, 2020, doi: 10.1109/ACCESS.2020.3016651.
[60] R. A. Pratiwi, S. Nurmaini, D. P. Rini, M. N. Rachmatullah, and A. Darmawahyuni, “Deep ensemble learning for skin lesions classification with convolutional neural network,” IAES Int. J. Artif. Intell., vol. 10, no. 3, pp. 563–570, 2021, doi: 10.11591/ijai.v10.i3.pp563-570.
[61] Z. Rahman and A. M. Ami, “A transfer learning based approach for skin lesion classification from imbalanced data,” Proc. 2020 11th Int. Conf. Electr. Comput. Eng. ICECE 2020, pp. 65–68, 2020, doi: 10.1109/ICECE51571.2020.9393155.
[62] J. A. Camacho-Gutiérrez, S. Solorza-Calderón, and J. Álvarez-Borrego, “Multi-class skin lesion classification using prism- and segmentation-based fractal signatures,” Expert Syst. Appl., vol. 197, no. November 2021, p. 116671, Jul. 2022, doi: 10.1016/j.eswa.2022.116671.
[63] S. M. Alizadeh and A. Mahloojifar, “Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features,” Int. J. Imaging Syst. Technol., vol. 31, no. 2, pp. 695–707, Jun. 2021, doi: 10.1002/ima.22490.
[64] M. M. Mijwil, “Skin cancer disease images classification using deep learning solutions,” Multimed. Tools Appl., vol. 80, no. 17, pp. 26255–26271, 2021, doi: 10.1007/s11042-021-10952-7.
[65] A. Noshad, A. Khonaksar, and M. Mohebbi, “SkinXNet: A DoG-based Model for Automatic Detection of Skin Lesion using Deep Learning,” in 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE, Apr. 2021, pp. 1–6. doi: 10.1109/IPRIA53572.2021.9483522.
[66] N. Gessert, M. Nielsen, M. Shaikh, R. Werner, and A. Schlaefer, “Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data,” MethodsX, vol. 7, p. 100864, 2020, doi: 10.1016/j.mex.2020.100864.
[67] E. O. Molina-Molina, S. Solorza-Calderón, and J. Álvarez-Borrego, “Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features,” Appl. Sci., vol. 10, no. 17, p. 5954, Aug. 2020, doi: 10.3390/app10175954.
[68] I. Iqbal, M. Younus, K. Walayat, M. U. Kakar, and J. Ma, “Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images,” Comput. Med. Imaging Graph., vol. 88, no. November 2020, p. 101843, Mar. 2021, doi: 10.1016/j.compmedimag.2020.101843.
[69] T. A. Putra, S. I. Rufaida, and J. S. Leu, “Enhanced Skin Condition Prediction through Machine Learning Using Dynamic Training and Testing Augmentation,” IEEE Access, vol. 8, pp. 40536–40546, 2020, doi: 10.1109/ACCESS.2020.2976045.
[70] M. A. Kassem, K. M. Hosny, and M. M. Fouad, “Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning,” IEEE Access, vol. 8, pp. 114822–114832, 2020, doi: 10.1109/ACCESS.2020.3003890.
[71] A. Gong, X. Yao, and W. Lin, “Classification for Dermoscopy Images Using Convolutional Neural Networks Based on the Ensemble of Individual Advantage and Group Decision,” IEEE Access, vol. 8, pp. 155337–155351, 2020, doi: 10.1109/ACCESS.2020.3019210.
[72] B. Bayram et al., “Classification of Skin Lesion Images with Deep Learning Approaches,” Balt. J. Mod. Comput., vol. 10, no. 2, pp. 241–250, 2022, doi: 10.22364/bjmc.2022.10.2.10.
[73] A. Naeem, M. S. Farooq, A. Khelifi, and A. Abid, “Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities,” IEEE Access, vol. 8, pp. 110575–110597, 2020, doi: 10.1109/ACCESS.2020.3001507.
[74] H. C. Reis, V. Turk, K. Khoshelham, and S. Kaya, “InSiNet: a deep convolutional approach to skin cancer detection and segmentation,” Med. Biol. Eng. Comput., vol. 60, no. 3, pp. 643–662, Mar. 2022, doi: 10.1007/s11517-021-02473-0.
[75] N. Nigar, M. Umar, M. K. Shahzad, S. Islam, and D. Abalo, “A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification,” IEEE Access, vol. 10, no. November, pp. 113715–113725, 2022, doi: 10.1109/ACCESS.2022.3217217.
[76] J. S M, M. P, C. Aravindan, and R. Appavu, “Classification of skin cancer from dermoscopic images using deep neural network architectures,” Multimed. Tools Appl., vol. 82, no. 10, pp. 15763–15778. 2022, doi: 10.1007/s11042-022-13847-3.
[77] S. Benyahia, B. Meftah, and O. Lézoray, “Multi-features extraction based on deep learning for skin lesion classification,” Tissue Cell, vol. 74, no. November 2021, p. 101701, Feb. 2022, doi: 10.1016/j.tice.2021.101701.
[78] S. Filipescu, A. Butacu, G. Tiplica, and D. Nastac, “Deep‐learning approach in the study of skin lesions,” Ski. Res. Technol., vol. 27, no. 5, pp. 931–939, Sep. 2021, doi: 10.1111/srt.13045.
[79] Cauvery, Siddalingaswamy, S. Pathan, and N. D’souza, “A Multiclass Skin Lesion classification approach using Transfer learning based convolutional Neural Network,” in 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), IEEE, Mar. 2021, pp. 1–6. doi: 10.1109/ICBSII51839.2021.9445175.
[80] F. Santos, F. Silva, and P. Georgieva, “Transfer Learning for Skin Lesion Classification using Convolutional Neural Networks,” in International Conference on Innovations in Intelligent SysTems and Applications (INISTA), IEEE, Aug. 2021, pp. 1–6. doi: 10.1109/INISTA52262.2021.9548455.
[81] H. M. Balaha and A. E.-S. Hassan, “Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm,” Neural Comput. Appl., vol. 9, pp. 815-853, Sep. 2022, doi: 10.1007/s00521-022-07762-9.
[82] Z. Liu, R. Xiong, and T. Jiang, “CI-Net: Clinical-Inspired Network for Automated Skin Lesion Recognition,” IEEE Trans. Med. Imaging, vol. 42, no. 3, pp. 619–632, Mar. 2023, doi: 10.1109/TMI.2022.3215547.
[83] S. S. Han, M. S. Kim, W. Lim, G. H. Park, I. Park, and S. E. Chang, “Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm,” J. Invest. Dermatol., vol. 138, no. 7, pp. 1529–1538, Jul. 2018, doi: 10.1016/j.jid.2018.01.028.
[84] Y. Fujisawa et al., “Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis,” Br. J. Dermatol., vol. 180, no. 2, pp. 373–381, 2019, doi: 10.1111/bjd.16924.
[85] A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Feb. 2017, doi: 10.1038/nature21056.
[86] C. E. Metz, “Basic principles of ROC analysis,” Semin. Nucl. Med., vol. 8, no. 4, pp. 283–298, Oct. 1978, doi: 10.1016/S0001-2998(78)80014-2.
[87] P. Tang et al., “Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging,” Comput. Methods Programs Biomed., vol. 178, pp. 289–301, 2019, doi: 10.1016/j.cmpb.2019.07.005.
[88] L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks,” IEEE Trans. Med. Imaging, vol. 36, no. 4, pp. 994–1004, Apr. 2017, doi: 10.1109/TMI.2016.2642839.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0