{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:50:39Z","timestamp":1776077439911,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000774","name":"Defense Threat Reduction Agency","doi-asserted-by":"publisher","award":["HDTRA1-15-1-0053"],"award-info":[{"award-number":["HDTRA1-15-1-0053"]}],"id":[{"id":"10.13039\/100000774","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IDBR-1556370"],"award-info":[{"award-number":["IDBR-1556370"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.<\/jats:p>","DOI":"10.3390\/sym11060790","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T11:15:58Z","timestamp":1560424558000},"page":"790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Skin Cancer Diagnostics with an All-Inclusive Smartphone Application"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8908-0961","authenticated-orcid":false,"given":"Upender","family":"Kalwa","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8617-7653","authenticated-orcid":false,"given":"Christopher","family":"Legner","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA"}]},{"given":"Taejoon","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4577-8567","authenticated-orcid":false,"given":"Santosh","family":"Pandey","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.artmed.2012.08.002","article-title":"Computerized analysis of pigmented skin lesions: A review","volume":"56","author":"Korotkov","year":"2012","journal-title":"Artif. Intell. Med."},{"key":"ref_2","first-page":"496202","article-title":"Novel method for border irregularity assessment in dermoscopic color images","volume":"2015","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Scharcanski, J., and Celebi, M.E. (2013). Computer Vision Techniques for the Diagnosis of Skin Cancer, Springer Science & Business Media.","DOI":"10.1007\/978-3-642-39608-3"},{"key":"ref_4","first-page":"1","article-title":"SKINcure: An Innovative Smartphone-Based Application to Assist in Melanoma Early Detection and Prevention","volume":"15","author":"Abuzaghleh","year":"2014","journal-title":"Signal Image Process. Int. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6578","DOI":"10.1016\/j.eswa.2015.04.034","article-title":"MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images","volume":"42","author":"Giotis","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.bspc.2011.01.003","article-title":"Hair removal methods: A comparative study for dermoscopy images","volume":"6","author":"Abbas","year":"2011","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","first-page":"e1","article-title":"Screening, early detection, education, and trends for melanoma: Current status (2007\u20132013) and future directions Part II. Screening, education, and future directions","volume":"71","author":"Mayer","year":"2014","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"301","DOI":"10.3322\/caac.20074","article-title":"The evolution of melanoma diagnosis: 25 years beyond the ABCDs","volume":"60","author":"Rigel","year":"2010","journal-title":"CA Cancer J. Clin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/j.1600-0846.2005.00092.x","article-title":"Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes","volume":"11","author":"Erkol","year":"2005","journal-title":"Skin Res. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Mar\u00e7al, A.R.S., Vieira, A., Nascimento, J.C., Silveira, M., Marques, J.S., and Rozeira, J. (2007, January 22\u201326). Comparison of segmentation methods for automatic diagnosis of dermoscopy images. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353865"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1002\/1097-0142(19950115)75:2+<684::AID-CNCR2820751411>3.0.CO;2-B","article-title":"Techniques of cutaneous examination for the detection of skin cancer","volume":"75","author":"Kopf","year":"1995","journal-title":"Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1016\/S0190-9622(94)70264-0","article-title":"Computer image analysis in the diagnosis of melanoma","volume":"31","author":"Green","year":"1994","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Massone, C., Hofmann-Wellenhof, R., Ahlgrimm-Siess, V., Gabler, G., Ebner, C., and Peter Soyer, H. (2007). Melanoma Screening with Cellular Phones. PLoS ONE, 2.","DOI":"10.1371\/journal.pone.0000483"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1111\/j.1365-2133.2011.10208.x","article-title":"Mobile teledermatology for skin tumour screening: Diagnostic accuracy of clinical and dermoscopic image tele-evaluation using cellular phones","volume":"164","author":"Kroemer","year":"2011","journal-title":"Br. J. Dermatol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Karargyris, A., Karargyris, O., and Pantelopoulos, A. (2012, January 7\u20139). DERMA\/Care: An advanced image-processing mobile application for monitoring skin cancer. Proceedings of the 24th International Conference on Tools with Artificial Intelligence, Athens, Greece.","DOI":"10.1109\/ICTAI.2012.180"},{"key":"ref_16","unstructured":"Do, T.T., Zhou, Y., Zheng, H., Cheung, N.M., and Koh, D. (2014, January 26\u201330). Early melanoma diagnosis with mobile imaging. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3745\/JIPS.02.0002","article-title":"Skin segmentation using YUV and RGB color spaces","volume":"10","author":"Rahmat","year":"2014","journal-title":"J. Inf. Process. Syst."},{"key":"ref_18","first-page":"1","article-title":"An Appropriate Color Space to Improve Human Skin Detection","volume":"9","author":"Ennehar","year":"2010","journal-title":"Infocomp"},{"key":"ref_19","first-page":"30","article-title":"Efficient Melanoma Detection Using Texture-Based RSurf Features","volume":"Volume 9730","author":"Majtner","year":"2016","journal-title":"International Conference on Image Analysis and Recognition"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Scharcanski, J., and Celebi, M.E. (2014). Melanoma Decision Support Using Lighting-Corrected Intuitive Feature Models. Computer Vision Techniques for the Diagnosis of Skin Cancer, Springer.","DOI":"10.1007\/978-3-642-39608-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/JSYST.2013.2271540","article-title":"Two systems for the detection of melanomas in dermoscopy images using texture and color features","volume":"8","author":"Barata","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_22","first-page":"468","article-title":"m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine","volume":"Volume 2","author":"Aleem","year":"2017","journal-title":"eHealth 360\u00b0"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cmpb.2016.03.032","article-title":"Computational methods for the image segmentation of pigmented skin lesions: A review","volume":"131","author":"Oliveira","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.compmedimag.2010.08.001","article-title":"Border detection in dermoscopy images using hybrid thresholding on optimized color channels","volume":"35","author":"Garnavi","year":"2011","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.eswa.2016.05.017","article-title":"A computational approach for detecting pigmented skin lesions in macroscopic images","volume":"61","author":"Oliveira","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.amc.2009.04.081","article-title":"An automatic based nonlinear diffusion equations scheme for skin lesion segmentation","volume":"215","author":"Barcelos","year":"2009","journal-title":"Appl. Math. Comput."},{"key":"ref_27","unstructured":"Deng, G., and Cahill, L.W. (November, January 31). An adaptive Gaussian filter for noise reduction and edge detection. Proceedings of the 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.compmedimag.2016.05.002","article-title":"Skin lesion image segmentation using Delaunay Triangulation for melanoma detection","volume":"52","author":"Pennisi","year":"2016","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_29","unstructured":"Blackledge, J., and Dubovitskiy, D.A. (2011, January 22\u201327). MoleTest TM: A Web-based Skin Cancer Screening System. Proceedings of the Third International Conference on Resource Intensive Applications and Services, Venice, Italy."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/JBHI.2015.2390032","article-title":"A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model","volume":"20","author":"Ma","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.procs.2015.04.209","article-title":"Computer aided melanoma skin cancer detection using image processing","volume":"48","author":"Jain","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TIP.2008.920737","article-title":"A real-time algorithm for the approximation of level-set-based curve evolution","volume":"17","author":"Shi","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"\u015euta, L., Bessy, F., Veja, C., and Vaida, M.F. (September, January 30). Active contours: Application to plant recognition. Proceedings of the 8th International Conference on Intelligent Computer Communication and Processing (ICCP 2012), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2012.6356183"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3322\/canjclin.35.3.130","article-title":"Early Detection of Malignant Melanoma: The Role of Physician Examination and Self-Examination of the Skin","volume":"35","author":"Friedman","year":"1985","journal-title":"Ca Cancer J. Clin."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1111\/j.1600-0846.2007.00181.x","article-title":"Combination of features from skin pattern and ABCD analysis for lesion classification","volume":"13","author":"She","year":"2007","journal-title":"Skin Res. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1111\/ajd.12143","article-title":"\u201cDo UC the melanoma?\u201d Recognising the importance of different lesions displaying unevenness or having a history of change for early melanoma detection","volume":"55","author":"Yagerman","year":"2014","journal-title":"Aust. J. Dermatol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/j.1600-0846.2009.00403.x","article-title":"A new method describing border irregularity of pigmented lesions","volume":"16","author":"Zhou","year":"2010","journal-title":"Skin Res. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"S\u00e1ez, A., Acha, B., and Serrano, C. (2014). Pattern Analysis in Dermoscopic Images. Computer Vision Techniques for the Diagnosis of Skin Cancer, Springer.","DOI":"10.1007\/978-3-642-39608-3_2"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/S0190-9622(87)70239-4","article-title":"In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions","volume":"17","author":"Pehamberger","year":"1987","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1111\/jdv.14129","article-title":"The impact of dermoscopy on melanoma detection in the practice of dermatologists in Europe: Results of a pan-European survey","volume":"31","author":"Forsea","year":"2017","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jaad.2006.09.003","article-title":"The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy","volume":"56","author":"Henning","year":"2007","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Leachman, S.A., Cassidy, P.B., Chen, S.C., Curiel, C., Geller, A., Gareau, D., Pellacani, G., Grichnik, J.M., Malvehy, J., and North, J. (2016). Methods of Melanoma Detection. Melanoma, Springer.","DOI":"10.1007\/978-3-319-22539-5_3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1001\/archderm.1996.03890340038007","article-title":"Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features","volume":"132","author":"Menzies","year":"1996","journal-title":"Arch. Dermatol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.jaad.2015.01.025","article-title":"Early detection of melanoma: Reviewing the ABCDEs","volume":"72","author":"Tsao","year":"2015","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1001\/jama.292.22.2771","article-title":"Early diagnosis of cutaneous melanoma: Revisiting the ABCD criteria","volume":"292","author":"Shaw","year":"2004","journal-title":"JAMA"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e014096","DOI":"10.1136\/bmjopen-2016-014096","article-title":"Diagnosing malignant melanoma in ambulatory care: A systematic review of clinical prediction rules","volume":"7","author":"Harrington","year":"2017","journal-title":"BMJ Open"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1001\/archderm.142.4.447","article-title":"Skills training to learn discrimination of ABCDE criteria by those at risk of developing melanoma","volume":"142","author":"Robinson","year":"2006","journal-title":"Arch. Dermatol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/j.jaad.2008.10.065","article-title":"Strategies for early melanoma detection: Approaches to the patient with nevi","volume":"60","author":"Grossman","year":"2009","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ramlakhan, K., and Shang, Y. (2011, January 7\u20139). A Mobile Automated Skin Lesion Classification System. Proceedings of the 23rd International Conference on Tools with Artificial Intelligence (2011 IEEE), Boca Raton, FL, USA.","DOI":"10.1109\/ICTAI.2011.29"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Doukas, C., Stagkopoulos, P., Kiranoudis, C.T., and Maglogiannis, I. (September, January 28). Automated skin lesion assessment using mobile technologies and cloud platforms. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346458"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pennisi, A., Bloisi, D.D., Nardi, D., Giampetruzzi, A.R., Mondino, C., and Facchiano, A. (2015, January 9\u201311). Melanoma Detection Using Delaunay Triangulation. Proceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, Italy.","DOI":"10.1109\/ICTAI.2015.117"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","article-title":"Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks","volume":"36","author":"Yu","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mendonca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., and Rozeira, J. (2013, January 3\u20137). PH2\u2014A dermoscopic image database for research and benchmarking. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_58","unstructured":"(2018, August 23). Cancer Facts & Figures. Available online: https:\/\/www.cancer.org\/content\/dam\/cancer-org\/research\/cancer-facts-and-statistics\/annual-cancer-facts-and-figures\/2018\/cancer-facts-and-figures-2018.pdf."},{"key":"ref_59","unstructured":"(2018, August 23). Cancer Facts & Figures. Available online: https:\/\/www.cancer.org\/content\/dam\/cancer-org\/research\/cancer-facts-and-statistics\/annual-cancer-facts-and-figures\/2014\/cancer-facts-and-figures-2014.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.patcog.2012.08.012","article-title":"Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm","volume":"46","author":"Xie","year":"2013","journal-title":"Pattern Recognit."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/790\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:58:16Z","timestamp":1760187496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":60,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["sym11060790"],"URL":"https:\/\/doi.org\/10.3390\/sym11060790","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,13]]}}}