IJBTT

A Review on Skin Cancer Detection and Classification using Infrared images

A Review on Skin Cancer Detection and Classification using Infrared images
	© 2022 by IJETT Journal
	Volume-70 Issue-4
	Year of Publication : 2022
	Authors : Akila Victor, Bhuvanjeet Singh Gandhi, Muhammad Rukunuddin Ghalib, Asha Jerlin M
	DOI :  10.14445/22315381/IJETT-V70I4P235

How to Cite?

Akila Victor, Bhuvanjeet Singh Gandhi, Muhammad Rukunuddin Ghalib, Asha Jerlin M, "A Review on Skin Cancer Detection and Classification using Infrared images," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 403-417, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P235

Abstract
Skin cancer is considered one of the most complex forms of cancer. If the skin cancer is not treated early, there is a high possibility that cancer could spread to different parts of the body. Melanoma skin cancer count has been increasing day by day. Early detection plays a very vital role in the treatment of cancer. However, present-day technological developments can detect skin cancer as early as possible. This review focuses on the characteristic features such as texture, shape, color, and structure, the essential paradigm for detecting skin cancer. In medical image processing, skin cancer detection at its initial stage can be done through computer-aided detection, artificial intelligence, swarm technique, etc. In the case of an automatic diagnosis system, there are, most importantly, two major steps, namely skin anomaly detection, and classification. We present a thorough review of skin cancer detection and classification using infrared imaging, artificial neural networks, Gaussian classifiers, etc. This review also delivers obligatory information on numerous techniques and primary steps for the automatic detection and classification of skin cancer.

Keywords
Artificial intelligence, Classification, Computer-aided detection, Infrared imaging, Melanoma skin cancer

Reference
[1] J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction, ser. Lecture Notes in Statistics. Berlin, Germany: Springer. 61(1) (1989) 200-220.
[2] M. Elgamal, Automatic Skin Cancer Image Classification, International Journal of Advanced Computer Science and Applications. 4(3) (2013) 287-294.
[3] A. K. Mittra and R. Parekh. Automated Detection of Skin Diseases Using Texture Features, International Journal of Engineering Science and Technology. 3(6) (2011) 4801-4808.
[4] D. Shen, C. Chiu, and P. Huang, Modified Laplacian Filter and Intensity Correction Technique for Image Resolution Enhancement, In the Proceedings of 5 IEEE International Conference on Multimedia and Expo (ICME 2006), Canada. 457-460.
[5] M. Sadeghi, T. K. Lee, D. McLean, H. Lui, and M. S. Atkins, Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions, IEEE Transactions on Medical Imaging. 32(5) (2013) 849-861.
[6] J. A. Jaleel, S. Salim, and Aswin. R. B, Computer-Aided Detection of Skin Cancer, In the Proceedings of International Conference on Circuits, Power and Computing Technologies, ICCPCT-2013. (2013) 1137-1142.
[7] Q. Abbas, M. E. Celebi, C. Serrano, I. F. Garcia, and G. Ma. Pattern Classification of Dermoscopy Images: A Perceptually Uniform Model, Pattern Recognition. 46(1) (2013) 86-97.
[8] J. Glaister, R. Amelard, A. Wong, and D. A. Clausi, MSIM: Multi-stage Illumination Modeling of Dermatological Photographs for Illumination Corrected Skin Lesion Analysis, IEEE Transactions on Biomedical Engineering. 60(7) (2013) 1873-1883.
[9] L. Ballerini, R. B. Fisher, B. Aldridge, and J. Rees, Color and Texture-Based Hierarchical K-NN Approach to the Classification of Non-Melanoma-Melanoma Skin Lesions, M. E. Celebi, G. Schaefer (eds.) Lecture Notes in Computational Vision and Biomechanics, Color Medical Image Analysis, Springer. 6 (2013) 63- 86.
[10] S. Jain, V. Jagtap, and N. Pise, Computer-Aided Melanoma Skin Cancer Detection Using Image Processing, in the Proceedings of International Conference on Intelligent Computing, Communication & Convergence (ICCC-2015), Procedia Computer Science, Bhubaneshwar, India. 48 (2015) 736-741.
[11] S. E. Godoy, D. A. Ramirez, S. A. Myers, G. Winckel, S. Krishna, M. Berwick, R. S. Padilla, P. Sen, and S. Krishna, Dynamic Infrared Imaging for Skin Cancer Screening. Infrared Physics & Technology, 70 (2015) 147-152.
[12] Abdel-Nasser M, Moreno A, & Puig D, Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods, Electronics. 8(1) (2019) 100.
[13] Yas Q. M, Zaidan A. A, Zaidan B. B, Hashim M, & Lim C. K, A Systematic Review on Smartphone Skin Cancer Apps: Coherent Taxonomy, Motivations, Open Challenges and Recommendations, and New Research Direction, Journal of Circuits, Systems, and Computers. 27(05) (2018) 1830003.
[14] Bezerra L. A, Oliveira M. M, Rolim T. L, Conci A, Santos F. G. S, Lyra P. R. M, & Lima R. C. F, Estimation of Breast Tumor Thermal Properties Using Infrared Images, Signal Processing. 93(10) (2013) 2851-2863.
[15] Kanzawa Y, Kimura Y, & Naito T, Human Skin Detection by Visible and Near-Infrared Imaging, IAPR Conference on Machine Vision Applications. 12 (2011) 14-22.
[16] Kakumanu P, Makrogiannis S, & Bourbakis N, A Survey of Skin-Color Modeling and Detection Methods, Pattern Recognition. 40(3) (2007) 1106-1122.
[17] Nunez A. S, & Mendenhall M. J, Detection of Human Skin in Near-Infrared Hyperspectral Imagery, In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium, IEEE. 2 (2008) II-621.
[18] Schaefer G, Tait R, & Zhu S. Y, Overlay of Thermal and Visual Medical Images Using Skin Detection and Image Registration, In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE. (2006) 965-967.
[19] Fredembach C, Barbuscia N, & Süsstrunk S, Combining Visible and Near-Infrared Images for Realistic Skin Smoothing, In Color and Imaging Conference, Society for Imaging Science and Technology. 2009 (2009) 242-247.
[20] Khan M. M, Ward R. D, & Ingleby M, Classifying Pretended and Evoked Facial Expressions of Positive and Negative Affective States Using Infrared Skin Temperature Measurement, ACM Transactions on Applied Perception (TAP). 6(1) (2009) 1-22.
[21] Acharya R, Ng E. Y. K, Yee G. C, Hua T. J, & Kagathi M, Analysis of Normal Human Eye with Different Age Groups Using Infrared Images, Journal of Medical Systems. 33(3) (2009) 207-213.
[22] Elgammal A, Muang C, & Hu D, Skin Detection - A Short Tutorial, Encyclopedia of Biometrics. 4 (2009) 1218-1224.
[23] Khan M. M, Ingleby M, & Ward R. D, Automated Facial Expression Classification and Affect Interpretation Using Infrared Measurement of Facial Skin Temperature Variations, ACM Transactions on Autonomous and Adaptive Systems (TAAS). 1(1) (2006) 91-113.
[24] Colak A, Polat B, Okumus Z, Kaya M, Yanmaz L. E, & Hayirli A, Early Detection of Mastitis Using Infrared Thermography in Dairy Cows, Journal of Dairy Science. 91(11) (2008) 4244-4248.
[25] Åberg P, Geladi P, Nicander I, Hansson J, Holmgren U, & Ollmar S, A Comparison between the Two Techniques is Skin Cancer`s Noninvasive and Micro-Invasive Electrical Impedance Spectra, Skin Research and Technology. 11(4) (2005) 281-286.
[26] De Leeuw J, Van Der Beek N, Neugebauer W. D, Bjerring P, & Neumann H. M, Fluorescence Detection and Diagnosis of Non-Melanoma-Melanoma Skin Cancer at an Early Stage, Lasers in Surgery and Medicine: The Official Journal of the American Society for Laser Medicine and Surgery. 41(2) (2009) 96-103.
[27] Bhanu D, & Balasubramanian P, A Predictive and Forecasting Model for Increased Sales Rule Mining Approach, ISAST Trans. on Intelligent Systems. (1).
[28] Lieber C. A, Majumder S. K, Ellis D. L, Billheimer D. D, & Mahadevan Jansen A, In Vivo Non-Melanoma-Melanoma Skin Cancer Diagnosis Using Raman Microspectroscopy, Lasers in Surgery and Medicine: The Official Journal of the American Society for Laser Medicine and Surgery. 40(7) (2008) 461-467.
[29] Mehta P, Chand K, Narayanswamy D, Beetner D. G, Zoughi R, & Stoecker W. V, Microwave Reflectometry is a Novel Diagnostic Tool for the Detection of Skin Cancers, IEEE Transactions on Instrumentation and Measurement. 55(4) (2006) 1309-1316.
[30] Fujiwara M, Mizukami T, Suzuki A, & Fukamizu H, Sentinel Lymph Node Detection in Skin Cancer Patients Using Real-Time Fluorescence Navigation with Indocyanine Green: Preliminary Experience, Journal of Plastic, Reconstructive & Aesthetic Surgery. 62(10) (2009) e373-e378.
[31] Maglogiannis I, & Doukas C. N, Overview of Advanced Computer Vision Systems for Skin Lesions Characterization, IEEE Transactions on Information Technology in Biomedicine. 13(5) (2009) 721-733.
[32] Yaroslavsky A. N, Barbosa J. G, Neel V, DiMarzio C. A, & Anderson R. R, Combining Multispectral Polarized Light Imaging and Confocal Microscopy for Localization of Non-Melanoma-Melanoma Skin Cancer, Journal of Biomedical Optics. 10(1) (2005) 014011.
[33] Esteva A, Kuprel B, Novoa R. A, Ko J, Swetter S. M, Blau H. M, & Thrun S, Dermatologist-Level Classification of Skin Cancer with Deep Neural Network, Nature. 542(7639) (2017) 115-118.
[34] Elgamal M, Automatic Skin Cancer Image Classification, (IJACSA), International Journal of Advanced Computer Science and Applications. 4(3) (2013) 287-294.
[35] Jain Y. K, & Jain M, Comparison between Different Classification Methods with Application to Skin Cancer, International Journal of Computer Applications. 53(11) (2012).
[36] Maragoudakis M, & Maglogiannis I, Skin Lesion Diagnosis from Images Using Novel Ensemble Classification Techniques, In Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, IEEE. (2010) 1-5.
[37] Masood A, & Ali Al-Jumaily A, Computer-Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms, International Journal of Biomedical Imaging. (2013).
[38] Sigurdsson S, Philipsen P. A, Hansen L. K, Larsen J, Gniadecka M, & Wulf H. C, Detection of Skin Cancer by Classification of Raman Spectra, IEEE Transactions on Biomedical Engineering. 51(10) (2004) 1784-1793.
[39] Sheha M. A, Mabrouk M. S, & Sharawy A, Automatic Detection of Melanoma Skin Cancer Using Texture Analysis, International Journal of Computer Applications. 42(20) (2012) 22-26.
[40] Maurya R, Singh S. K, Maurya A. K, & Kumar A, GLCM and Multi-Class Support Vector Machine-Based Automated Skin Cancer Classification, In 2014 International Conference on Computing for Sustainable Global Development (INDIACom), IEEE. (2014) 444-447.
[41] Farooq M. A, Azhar M. A. M, & Raza R. H, An Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers, The 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), IEEE. (2016) 301-308.
[42] Rezvantalab A, Safigholi H, & Karimijeshni S, Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Network Algorithms, Arxiv Preprint arXiv:1810.10348. (2018).
[43] Victor A, & Ghalib M, Automatic Detection and Classification of Skin Cancer, International Journal of Intelligent Engineering and Systems. 10(3) (2017) 444-451.
[44] Alquran H, Qasmieh I. A, Alqudah A. M, Alhammouri S, Alawneh E, Abughazaleh A, & Hasayen F, The Melanoma Skin Cancer Detection and Classification Using a Support Vector Machine, In 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE. (2017) 1-5.
[45] Taufiq M. A, Hameed N, Anjum A, & Hameed F, M-Skin Doctor: A Mobile-Enabled System for Early Melanoma Skin Cancer Detection Using a Support Vector Machine, In eHealth 360°, Springer, Cham. (2017) 468-475.
[46] Hosny K. M, Kassem M. A, & Foaud M. M, Skin Cancer Classification Using Deep Learning and Transfer Learning, In 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), IEEE. (2018) 90-93.
[47] Ansari U. B, & Sarode T, Skin Cancer Detection Using Image Processing, Int Res J Eng Technol. 4(4) (2017) 2875-2881.
[48] Katre P. R, & Thakare A, Detection of Lung Cancer Stages Using Image Processing and Data Classification Techniques, In 2017 2nd International Conference for Convergence in Technology (I2CT), IEEE. (2017) 402-404).
[49] Khan M. Q, Hussain A, Rehman S. U, Khan U, Maqsood M, Mehmood K, & Khan M. A, Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer, IEEE. 7 (2019) 90132-90144.
[50] Pham T. C, Tran G. S, Nghiem T. P, Doucet A, Luong C. M, & Hoang V. D, A Comparative Study for Classification of Skin Cancer, In 2019 International Conference on System Science and Engineering (ICSSE), IEEE. (2019) 267-272.
[51] Yadav V, & Kaushik V. D, A Study on Automatic Early Detection of Skin Cancer, International Journal of Advanced Intelligence Paradigms. 12(3-4) (2019) 392-399.
[52] Angurana N, Rajan A. P, & Srivastava I, Skin Cancer Detection and Classification, International Journal of Engineering and Management Research. 9 (2019).
[53] Amin J, Sharif A, Gul N, Anjum M. A, Nisar M. W, Azam F, & Bukhari S. A. C, Integrated Design of Deep Features Fusion for Localization and Classification of Skin Cancer, Pattern Recognition Letters. 131 (2020) 63-70.
[54] Saba T, Khan M. A, Rehman A, & Marie-Sainte S. L, Region Extraction and Classification of Skin Cancer: A Heterogeneous Framework of Deep CNN Features Fusion and Reduction, Journal of Medical Systems. 43(9) (2019) 289.
[55] Murugan A, Nair S. A. H, & Kumar K. S, Detection of Skin Cancer using SVM, Random Forest, and KNN Classifiers, Journal of Medical Systems. 43(8) (2019) 269.
[56] Badash I, Shauly O, Lui C. G, Gould D. J, & Patel K. M, Nonmelanoma Facial Skin Cancer: A Review of Diagnostic Strategies, Surgical Treatment, and Reconstructive Techniques, Clinical Medicine Insights: Ear, Nose and Throat. 12 (2019) 1179550619865278.
[57] Alom M. Z, Aspiras T, Taha T. M, & Asari V, Skin Cancer Segmentation and Classification with NABLA-N and Recurrent, Residual Convolutional Networks. Arxiv Preprint Arxiv: 1904.11126. (2019).
[58] Albahar M. A, Skin Lesion Classification Using a Convolutional Neural Network with a Novel Regularizer, IEEE Access. 7 (2019) 38306-38313.
[59] Sreelatha T, Subramanyam M. V, & Prasad M. G, Early Detection of Skin Cancer Using Melanoma Segmentation Technique, Journal of Medical Systems. 43(7) (2019) 190.
[60] Xie P, Zuo K, Zhang Y, Li F, Yin M, & Lu K, Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study. arXiv Preprint arXiv:1904.06156. (2019).
[61] Serte S, & Demirel H, Gabor Wavelet-Based Deep Learning for Skin Lesion Classification, Computers in Biology and Medicine. 113 (2019) 103423.
[62] Afza F, Khan M. A, Sharif M, & Rehman A, Microscopic Skin Laceration Segmentation and Classification: A Framework of Statistical Normal Distribution and Optimal Feature Selection. Microscopy Research and Technique. 82(9) (2019) 1471-1488.
[63] Khan M. A, Sharif M, Akram T, Bukhari S. A. C, & Nayak R. S, Developed Newton-Raphson-Based Deep Features Selection Framework for Skin Lesion Recognition, Pattern Recognition Letters. 129 (2020) 293-303.
[64] Jianu S. R. S, Ichim L, & Popescu D, Automatic Diagnosis of Skin Cancer Using Neural Networks, In 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE), IEEE. (2019) 1-4.
[65] Mohamed A, Mohamed W. A, & Zekry A. H, Deep Learning Can Improve Early Skin Cancer Detection, International Journal of Electronics and Telecommunications. 65 (2019).
[66] Lynn N. C. & War N, Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification, International Journal of Computer Science and Network. 8(3) (2019).
[67] Khan M. A, Javed M. Y, Sharif M, Saba T, & Rehman A, Multi-Model Deep Neural Network-Based Features Extraction and Optimal Selection Approach for Skin Lesion Classification, In 2019 International Conference on Computer and Information Sciences (ICCIS), IEEE. (2019) 1-7.
[68] Abbas Z, Rehman M. U, Najam S, & Rizvi S. D, An Efficient Gray-Level Co-Occurrence Matrix (GLCM) Based Approach Toward Skin Lesion Classification, In 2019 Amity International Conference on Artificial Intelligence (AICAI), IEEE. (2019) 317-320.
[69] Hameed N, Shabut A. M, Ghosh M. K, & Hossain M. A, Multi-Class Multi-Level Classification Algorithm for Skin Lesions Classification Using Machine Learning Techniques, Expert Systems with Applications. 141 (2020) 112961.
[70] Hosny K. M, Kassem M. A, & Foaud M. M, Classification of Skin Lesions Using Transfer Learning and Augmentation with Alex-Net, Plos One. 14(5) (2019) e0217293.
[71] Pandey P, Saurabh P, Verma B, & Tiwari B, A Multi-Scale Retinex with Color Restoration (MSR-CR) Technique for Skin Cancer Detection, In Soft Computing for Problem Solving, Springer, Singapore. (2019) 465-473.
[72] Aydinalp C, Joof S, Yilmaz T, Özsobaci N. P, Alkan F. A, & Akduman I, In Vitro Dielectric Properties of Rat Skin Tissue for Microwave Skin Cancer Detection, In 2019 International Applied Computational Electromagnetics Society Symposium (ACES), IEEE. (2019) 1-2.
[73] Jaiswar S, Kadri M, & Gatty V, Skin Cancer Detection Using Digital Image Processing, International Journal of Scientific Engineering and Research. 3(6) (2015) 138-140.
[74] Bratchenko I. A, Artemyev D. N, Myakinin O. O, Khristoforova Y. A, Moryatov A. A, Kozlov S. V, & Zakharov V. P, Combined Raman and Autofluorescence Ex Vivo Skin Cancer Diagnostics in Near-Infrared and Visible Regions, Journal of Biomedical Optics. 22(2) (2017) 027005.
[75] Paliwal N, Skin Cancer Segmentation, Detection and Classification Using Hybrid Image Processing Technique, International Journal of Engineering and Applied Sciences. 3(4) (2017).
[76] Khamparia A, Singh P. K, Rani P, Samanta D, Khanna A, & Bhushan B, An Internet of Health Things Driven Deep Learning Framework for Skin Cancer Detection and Classification Using Transfer Learning, Transactions on Emerging Telecommunications Technologies. (2020) e3963.
[77] Ma Z, & Tavares J. M. R, A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices, Journal of Medical Systems. 39(11) (2015) 177.
[78] Oliveira R. B, Papa J. P, Pereira A. S, & Tavares J. M. R, Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends, Neural Computing and Applications. 29(3) (2018) 613-636.
[79] Hameed N, Ruskin A, Hassan K. A, & Hossain M. A, A Comprehensive Survey on Image-Based Computer-Aided Diagnosis Systems for Skin Cancer, In 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), IEEE. (2016) 205-214.
[80] Herman C, & Cetingul M. P, Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging, JOVE (Journal of Visualized Experiments). (51) (2011) e2679.
[81] Akbari H, Uto K, Kosugi Y, Kojima K, & Tanaka N, Cancer Detection Using Infrared Hyperspectral Imaging, Cancer Science. 102(4) (2011) 852-857.
[82] Choudhury D, Naug A, & Ghosh S, Texture and Color Feature-Based WLS Framework Aided Skin Cancer Classification Using MSVM and ELM, In 2015 Annual IEEE India Conference (INDICON), IEEE. (2015) 1-6.
[83] Godoy S. E, Ramirez D. A, Myers S. A, Von Winckel G, Krishna S, Berwick M, & Krishna S, Dynamic Infrared Imaging for Skin Cancer Screening, Infrared Physics & Technology. 70 (2015) 147-152.
[84] Drakaki E, Vergou T, Dessinioti C, Stratigos A. J, Salavastru C, & Antoniou C, Spectroscopic Methods for the Photo Diagnosis of Non-Melanoma-Melanoma Skin Cancer, Journal of Biomedical Optics. 18(6) (2012) 061221.
[85] Inostroza F, Díaz S, Cárdenas J, Godoy S. E, & Figueroa M, Embedded Registration of Visible and Infrared Images in Real-Time for Noninvasive Skin Cancer Screening, Microprocessors and Microsystems. 55 (2017) 70-81.
[86] Peñaranda F, Naranjo V, Kastl L, Kemper B, Lloyd G. R, Nallala J, & Schnekenburger J, Multivariate Classification of Fourier Transforms Infrared Hyperspectral Images of Skin Cancer Cells, In 24th European Signal Processing Conference (EUSIPCO), IEEE. (2016) 1328-1332).
[87] Lessa V, & Marengoni M, Applying an Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images, In International Conference on Computer Vision and Graphics, Springer, Cham. (2016) 429-438.
[88] Zherdeva L. A, Bratchenko I. A, Myakinin O. O, Moryatov A. A, Kozlov S. V, & Zakharov V. P, In vivo Hyperspectral Imaging and Differentiation of Skin Cancer, In Optics in Health Care and Biomedical Optics VII. International Society for Optics and Photonics. 10024 (2016) 100244G.
[89] Mahdiraji S. A, Baleghi Y, & Sakhaei S. M, Skin Lesion Image Classification Using New Color Pigmented Boundary Descriptors, In 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE. (2017) 102-107.
[90] Yang J, Lu W, & Waibel A, Skin-Color Modeling and Adaptation, In Asian Conference on Computer Vision, Springer, Berlin, Heidelberg. (1998) 687-694.
[91] Yang, M. H, & Ahuja N, A Gaussian Mixture Model for Human Skin Color and its Applications in Image and Video Databases, In Storage and Retrieval for Image and Video Databases VII, Internationa. 3656 (1998) 458-466.
[92] Goel R, & Singh S, Skin Cancer Detection Using Glcm Matrix Analysis and Backpropagation Neural Network Classifier, International Journal of Computer Applications. 112(9) (2015) 40-47.
[93] Tschandl P, Rosendahl C, Akay B. N, Argenziano G, Blum A, Braun R. P, & Lapins J, Expert-Level Diagnosis of Non-Pigmented Skin Cancer by Combined Convolutional Neural Networks, JAMA Dermatology. 155(1) (2019) 58-65.
[94] Thanh D. N, Prasath V. S, & Hien N. N, Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Color Normalization, and Feature Extraction with the ABCD Rule, Journal of Digital Imaging. (2019) 1-12.
[95] Odeh S. M, Using an Adaptive Neuro-Fuzzy Inference System(AnFis) Algorithm for Automatic Diagnosis of Skin Cancer, Journal of Communication and Computer. 8(9) (2011) 751-755.
[96] Razmjooy N, Sheykhahmad F. R, & Ghadimi N, A Hybrid Neural Network–World Cup Optimization Algorithm for Melanoma Detection, Open Medicine. 13(1) (2018) 9-16.
[97] Aswin R. B, Jaleel J. A, & Salim S, Hybrid Genetic Algorithm—Artificial Neural Network Classifier for Skin Cancer Detection, In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), IEEE. (2018) 1304-1309.
[98] Jaisakthi S. M, Mirunalini P, & Aravindan C, Automated Skin Lesion Segmentation of Dermoscopic Images Using Grabcut and K-Means Algorithms, IET Computer Vision. 12(8) (2018) 1088-1095.
[99] Farooq M. A, Azhar M. A. M, & Raza R. H, An Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers, The IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), IEEE. (2016) 301-308.
[100] Elgamal M, Automatic Skin Cancer Image Classification, (IJACSA) International Journal of Advanced Computer Science and Applications. 4(3) (2013) 287-294.
[101] Glaister J, Amelard R, Wong A, & Clausi D. A, MSIM: Multi-stage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis, IEEE Transactions on Biomedical Engineering. 60(7) (2013) 1873-1883.
[102] Sadeghi M, Lee T. K, McLean D, Lui H, & Atkins M. S, Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions, IEEE Transactions on Medical Imaging. 32(5) (2013) 849-861.
[103] Huang G. B, Zhou H, Ding X, & Zhang R, Extreme Learning Machine for Regression and Multi-Class Classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 42(2) (2011) 513-529.
[104] Jain Y. K, & Jain M, Skin Cancer Detection and Classification Using Wavelet Transform and Probabilistic Neural Network. (2012).
[105] Blackledge J. M, & Dubovitskiy D. A, Object Detection and Classification with Applications to Skin Cancer Screening, Intelligent Systems. 1(1) (2008) 34-45.
[106] Lau H. T, & Al-Jumaily A, Automatically Early Detection of Skin Cancer: Study Based on Neural Network Classification, In International Conference of Soft Computing and Pattern Recognition, IEEE. (2009) 375-380.
[107] Angurana N, Rajan A. P, & Srivastava I, Skin Cancer Detection and Classification, International Journal of Engineering and Management Research. 9 (2019).
[108] Jain S, & Pise N, Computer-Aided Melanoma Skin Cancer Detection Using Image Processing, Procedia Computer Science. 48 (2015) 735-740.
[109] Victor A, & Ghalib M, Automatic Detection and Classification of Skin Cancer, International Journal of Intelligent Engineering and Systems. 10(3) (2017) 444-451.
[110] Suganya R, An Automated Computer-Aided Diagnosis of Skin Lesions Detection and Classification for Dermoscopy Images, In 2016 International Conference on Recent Trends in Information Technology (ICRTIT), IEEE. (2016) 1-5.
[111] Jaleel J. A, Salim S, & Aswin R. B, Computer-Aided Detection of Skin Cancer, In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), IEEE. (2013) 1137-1142).
[112] Alfed N, Khelifi F, Bouridane A, & Seker H, Pigment Network-Based Skin Cancer Detection, In 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE. (2015) 7214-7217.
[113] Jaworek-Korjakowska J, & Tadeusiewicz R, Determination of Border Irregularity in Dermoscopic Color Images of Pigmented Skin Lesions, In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE. (2014) 6459-6462.
[114] Codella N. C, Gutman D, Celebi M. E, Helba B, Marchetti M. A, Dusza S. W, & Halpern A, Skin Lesion Analysis toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC), In IEEE 15th International Symposium on Biomedical Imaging (ISBI), IEEE. (2018) 168-172.
[115] Alquran H, Qasmieh I. A, Alqudah A. M, Alhammouri S, Alawneh E, Abughazaleh A, & Hasayen F, The Melanoma Skin Cancer Detection and Classification Using a Support Vector Machine, In IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE. (2017) 1-5.
[116] Dubal P, Bhatt S, Joglekar C, & Patil S, Skin Cancer Detection and Classification, In 6th International Conference on Electrical Engineering and Informatics (ICEEI), IEEE. (2017) 1-6.
[117] Angurana N, Rajan A. P, & Srivastava I, Skin Cancer Detection and Classification, International Journal of Engineering and Management Research. 9 (2019).
[118] Jain S, & Pise N, Computer-Aided Melanoma Skin Cancer Detection Using Image Processing, Procedia Computer Science. 48 (2015) 735-740.