Automatic eczema classification in clinical images based on hybrid deep neural network
Tài liệu tham khảo
Hollestein, 2014, An insight into the global burden of skin diseases, J. Invest. Dermatol., 134, 1499, 10.1038/jid.2013.513
Picardi, 2013, Suicide risk in skin disorders, Clin. Dermatol., 31, 47, 10.1016/j.clindermatol.2011.11.006
Ma, 2015, A novel approach to segment skin lesions in dermoscopic images based on a deformable model, IEEE J. Biomed. Health Inf., 20, 615, 10.1109/JBHI.2015.2390032
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097
Egmont-Petersen, 2002, Image processing with neural networks—a review, Pattern Recogn., 35, 2279, 10.1016/S0031-3203(01)00178-9
Mishra NK, Celebi ME. An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning. arXiv preprint arXiv:1601.07843. 2016 Jan 28.
He, 2016, Deep residual learning for image recognition, 770
Fabbrocini, 2011, Teledermatology: from prevention to diagnosis of nonmelanoma and melanoma skin cancer, Int. J. Telemed. Appl., 2011
Foraker, 2015, EHR-based visualization tool: adoption rates, satisfaction, and patient outcomes, eGEMs, 3, 10.13063/2327-9214.1159
Fink, 2020, Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas, J. Eur. Acad. Dermatol. Venereol., 34, 1355, 10.1111/jdv.16165
Argenziano, 2001, Dermoscopy of pigmented skin lesions–a valuable tool for early, Lancet Oncol., 2, 443, 10.1016/S1470-2045(00)00422-8
Pehamberger, 1987, In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions, J. Am. Acad. Dermatol., 17, 571, 10.1016/S0190-9622(87)70239-4
Balch, 2001, Final version of the American Joint Committee on Cancer staging system for cutaneous melanoma, J. Clin. Oncol., 19, 3635, 10.1200/JCO.2001.19.16.3635
MacKie, 1990, Clinical recognition of early invasive malignant melanoma, BMJ Br. Med. J. (Clin. Res. Ed.), 301, 1005, 10.1136/bmj.301.6759.1005
Nasr-Esfahani, 2016, Melanoma detection by analysis of clinical images using convolutional neural network, 1373
Yu, 2016, Automated melanoma recognition in dermoscopy images via very deep residual networks, IEEE Trans. Med. Imag., 36, 994, 10.1109/TMI.2016.2642839
Pham, 2018, Deep CNN and data augmentation for skin lesion classification, 573
Kostopoulos, 2017, Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases, Int. J. Med. Inf., 105, 10.1016/j.ijmedinf.2017.05.016
Bajaj, 2018, Automated system for prediction of skin disease using image processing and machine learning, Int. J. Comput. Appl., 180, 9
Kawahara J, BenTaieb A, Hamarneh G. Deep features to classify skin lesions. In2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) 2016 Apr 13 (pp. 1397-1400). IEEE.
Ge Z, Demyanov S, Bozorgtabar B, Abedini M, Chakravorty R, Bowling A, Garnavi R. Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging. In2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 2017 Apr 18 (pp. 986-990). IEEE.
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature
Amarathunga, 2015, Expert system for diagnosis of skin diseases, Int. J. Sc. Technol. Res., 4, 174
Masood, 2013, Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms, Int. J. Biomed. Imag., s
Majtner, 2016, Combining deep learning and handcrafted features for skin lesion classification, 1
Chakraborty, 2017, Image based skin disease detection using hybrid neural network coupled bag-of-features, 242
Zhou, 2017, Multi-classification of skin diseases for dermoscopy images using deep learning, 1
Hameed N, Shabut AM, Hossain MA. Multiclass skin diseases classification using deep convolutional neural network and support vector machine. In2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) 2018 Dec 3 (pp. 1-7). IEEE.
Srivastava, 2018, Automatic detection of eczema using image processing, 18, 171
Alam MN, Munia TT, Tavakolian K, Vasefi F, MacKinnon N, Fazel-Rezai R. Automatic detection and severity measurement of eczema using image processing. In2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2016 Aug 16 (pp. 1365-1368). IEEE.
Mendonça, 2013, PH 2-A dermoscopic image database for research and benchmarking, 5437
Giotis, 2015, MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images, Expert Syst. Appl., 42, 6578, 10.1016/j.eswa.2015.04.034
Argenziano, 2000
Li, 2016
Li, 2018, Skin lesion analysis towards melanoma detection using deep learning network, Sensors, 18, 556, 10.3390/s18020556
Sun, 2019, Deeply-supervised knowledge synergy, 6997
Junayed, 2020, Eczemanet: a deep cnn-based eczema diseases classification, 174
Velasco, 2019
Kalbande, 2020, An artificial intelligence approach for the recognition of early stages of eczema, Int. J. Med. Eng. Inf., 12, 52
Khan, 2016, A comparison of deep learning and hand crafted features in medical image modality classification, 633
Saba, 2021, Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features, Microsc. Res. Tech., 84, 1272, 10.1002/jemt.23686
Gotlieb, 1990, Texture descriptors based on co-occurrence matrices, Comput. Vis. Graph Image Process, 51, 70, 10.1016/S0734-189X(05)80063-5
Oliva, 2001, Modeling the shape of the scene: a holistic representation of the spatial envelope, Int. J. Comput. Vis., 42, 145, 10.1023/A:1011139631724
Dalal, 2005, Histograms of oriented gradients for human detection, 1, 886
Bosch, 2007, Representing shape with a spatial pyramid kernel, 401
Zoph, 2018, Learning transferable architectures for scalable image recognition, 8697
Xie, 2017, Aggregated residual transformations for deep neural networks, 1492
Szegedy, 2016, Rethinking the inception architecture for computer vision, 2818
Chollet, 2017, Xception: deep learning with depthwise separable convolutions, 1251
Szegedy, 2017, Inception-v4, inception-resnet and the impact of residual connections on learning
Robnik-Šikonja, 2003, Theoretical and empirical analysis of ReliefF and RReliefF, Mach. Learn., 53, 23, 10.1023/A:1025667309714
Kononenko, 1994, Estimating attributes: analysis and extensions of RELIEF, 171
Trevethan, 2017, Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice, Front. Public Health, 5, 307, 10.3389/fpubh.2017.00307
Narla, 2018, Automated classification of skin lesions: from pixels to practice, J. Invest. Dermatol., 138, 2108, 10.1016/j.jid.2018.06.175
Nar, 2018
Perez, 2018, Data augmentation for skin lesion analysis, 303
Collier, 2018, Progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images, 763
Selvaraju, 2017, Grad-cam: visual explanations from deep networks via gradient-based localization, 618