[1]
Orthaber K, Pristovnik MD, Skok K. Skin cancer and its treatment: Novel treatment approaches with emphasis on nanotechnology. J Nanomater 2017; 2017(2): 1-20.
[3]
Dubai P, Bhatt S, Joglekar C, Patii S. Skin cancer detection and classification. Proc 2017 6th Int Conf Electr Eng Informatics Sustain Soc Through Digit Innov ICEEI 2018; 2017: 1-6.
[7]
Khushmeen Brar A, Samant P. Review of an automated clinical decision support system for skin abrasion recognition and classification IJRAR 2019; 6(1): 511-17.
[8]
Abraham A, Sobhanakumari K, Mohan A. Artificial intelligence in dermatology. J Ski Sex Transm Dis 2021; 3(1): 99-102.
[9]
Corona R, Sera F, Binder M, Cerroni L. Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the internet. J Am Acad Dermatol 2003; 48(5): 679-93.
[11]
Pickert A. Basic Dermoscopy for the Resident CUTIS CUTIS 2012; 89: 1-6.
[13]
Mendonca T, Pedro MF, Jorge SM, Andr´e RS, Jorge R. PH2 - A dermoscopic image database for research and benchmarking. 35th Annual International Conference of the IEEE EMBS Osaka, Japan. 1967; pp. 1967; 14(4): 677-82.
[17]
Gutman D, et al. Skin lesion analysis toward melanoma detection : A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC) 2016.
[18]
Noel CF, Gutman D, Emre Celebi M, Helba B, Marchetti MA, Dusza SW. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) 2017; 1-5.
[19]
Codella NR, Tschand NV, Celebi P, Dusza ME, Gutman S. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). Preprint 2018; 1-12.
[20]
Tschandl P, Rosendahl C, Kittler H. Data Descriptor : The HAM 10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Nat Publ Gr 2018; 5: 1-9.
[27]
Rajesh A. Classification of malignant melanoma and Benign Skin Lesion by using back propagation neural network and ABCD rule. Cluster Comput 2018; 1-8.
[30]
Tabassum T, Munia K, Alam N, Neubert J, Fazel-rezai R, Member S. Automatic diagnosis of melanoma using linear and nonlinear features from digital image. Annu Int Conf IEEE Eng Med Biol Soc. 2017; 2017: pp. 4281-4.
[31]
Punal D. Computer vision for diagnosis of malignant melanoma by pixel intensity matrix parameters. 10th International Conference on Intelligent Systems and Control Coimbatore, India. 2016; pp. 2016; 7: 2-5.
[36]
Yu Z, Jiang X, Member S, Zhou F, Qin J, Ni D. Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Trans Biomed Eng 2018; 1.
[37]
Afza F, Sharif M, Mittal M, Attique M, Hemanth D J. A hierarchical three-step superpixels and deep learning framework for skin lesion classification. Methods 2022; 202: 88-102.
[38]
Ali S, Miah S, Haque J, Rahman M. Machine Learning with Applications An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach Learn with Appl 2021; 5(April): 100036.
[39]
Kumar EP, Sharma EP. Artificial neural networks-a study 2014; 2(2): 143-8.
[40]
Arasi MA, Salem AM. Malignant Melanoma Detection and Diagnosis. 2017; pp. 55-61.
[41]
Tumpa PP, Kabir A. An Artificial Neural Network Based Detection and Classification of Melanoma Skin Cancer Using Hybrid Texture Features. Sensors Int 2021; p. 100128.
[42]
Khan MA, Zhang Y, Sharif M, Akram T. Pixels to Classes: Intelligent learning framework for multiclass skin lesion localization and classification. Comput Electr Eng 2021; 90: 106956.
[44]
Hekler A. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 2019; Vol. 120: 114-21.
[46]
Piramanayagam S, Saber E, Schwartzkopf W, Koehler FW. Supervised classification of multisensor remotely sensed images using a deep learning framework. Computer Science, Environmental Science 2018; 1-25.
[52]
Sanjar K, Bekhzod O, Kim J, Kim J, Paul A, Kim J. Improved U-Net: Fully convolutional network model for skin-lesion segmentation. Appl Sci 2020; 10(10): 3658.
[55]
Albahli S. Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour. IEEE Access 2020; Vol. 8: 198403-14.
[59]
Roman CM, Schlager JG, Haggenmu¨ller S, von Kalle C, Utikal JS, Meier F. A benchmark for neural network robustness in skin cancer classification. 2021; 155: 191-9.
[60]
Rahman Z, Hossain S, Islam R, Hasan M. Informatics in Medicine Unlocked An approach for multiclass skin lesion classification based on ensemble learning. 2021; Vol. 25.
[62]
Moradi N, Mahdavi-amiri N. Biomedical signal processing and control multi-class segmentation of skin lesions via joint dictionary learning. Biomed Signal Process Control 2021; 68: 102787.
[65]
Ikuma Y. Production of the grounds for melanoma clasification using adaptive fuzzy inference neural network IEEE International Conference on Systems, Man, and Cybernetics Manchester, UK. 2013; pp. vol. 13: 2570-5.2013;
[70]
Hoshyar AN, Al-Jumaily A, Sulaiman R. Review on automatic early skin cancer detection 2011 International Conference on Computer Science and Service System (CSSS). Nanjing, China 2011; pp. 4036-39.
[73]
Chatterjee S. Mathematical morphology aided shape. Texture and Colour Feature Extraction from Skin Lesion for Identification of Malignant Melanoma 2015; pp. 200-3.
[77]
Yasmin JHJ, Sathik MM, Beevi SZ. Robust Segmentation Algorithm using LOG Edge Detector for Effective Border Detection of Noisy Skin Lesions.2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). Tirunelveli, India 2011; pp. 60-5.
[78]
Abuzaghleh O, Barkana BD, Faezipour M. SKINcure: A real time image analysis system to aid in the malignant melanoma prevention and early detection2014 Southwest Symposium on Image Analysis and Interpretation. San Diego, CA, USA 2014; pp. 85-8.