Journal ArticleDOI
Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule
Reda Kasmi,Karim Mokrani +1 more
TLDR
Automatic ABCD scoring of dermoscopy lesions is implemented and the experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.Abstract:
The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.read more
Citations
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Journal ArticleDOI
Skin lesion classification with ensembles of deep convolutional neural networks.
TL;DR: Wang et al. as mentioned in this paper proposed the aggregation of robust convolutional neural networks (CNNs) into one framework, where the final classification is achieved based on the weighted output of the member CNNs.
Journal ArticleDOI
Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features
TL;DR: This paper presents a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy that is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data.
Journal ArticleDOI
Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review
TL;DR: A review of the state of art techniques used in computer-aided diagnostic systems for dermoscopy, by giving the domain aspects of melanoma followed by the prominent Techniques used in each of the steps, and presents cognizance to judge the consequentiality of every methodology utilized in the literature.
Journal ArticleDOI
An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.
Muhammad Nasir,Muhammad Attique Khan,Muhammad Sharif,Ikram Ullah Lali,Tanzila Saba,Tassawar Iqbal +5 more
TL;DR: The proposed method detects and classifies melanoma significantly good as compared to existing methods and has provided promising results of sensitivity 97.7%, specificity 96.7, accuracy 97.5%, and F‐score 97.
Book ChapterDOI
A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images
TL;DR: This paper introduces a benchmark dataset for clinical skin diseases, which is currently the largest for visual recognition of skin diseases and performs extensive analyses on this dataset using the state of the art methods including CNNs.
References
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Journal ArticleDOI
Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010
Rafael Lozano,Mohsen Naghavi,Kyle J Foreman,Stephen S Lim,Kenji Shibuya,Victor Aboyans,Jerry Abraham,Tim Adair,Rakesh Aggarwal,Stephanie Y. Ahn,Mohammad A. AlMazroa,Miriam Alvarado,H. Ross Anderson,Laurie M. Anderson,Kathryn G. Andrews,Charles Atkinson,Larry M. Baddour,Suzanne Barker-Collo,David Bartels,Michelle L. Bell,Emelia J. Benjamin,Derrick A Bennett,Kavi Bhalla,Boris Bikbov,Aref A. Bin Abdulhak,Gretchen L. Birbeck,Fiona M. Blyth,Ian Bolliger,Soufiane Boufous,Chiara Bucello,Michael Burch,Peter Burney,Jonathan R. Carapetis,Honglei Chen,David Chou,Sumeet S. Chugh,Luc E. Coffeng,Steven D. Colan,Samantha M. Colquhoun,K. Ellicott Colson,John R. Condon,Myles Connor,Myles Connor,Leslie T. Cooper,Matthew A. Corriere,Monica Cortinovis,Karen Courville De Vaccaro,William G. Couser,Benjamin C Cowie,Michael H. Criqui,Marita Cross,Kaustubh Dabhadkar,Nabila Dahodwala,Diego De Leo,Louisa Degenhardt,Allyne Delossantos,Julie O. Denenberg,Don C. Des Jarlais,Samath D Dharmaratne,Samath D Dharmaratne,E. Ray Dorsey,Tim Driscoll,Herbert C. Duber,Beth E. Ebel,Patricia J. Erwin,Patricia Espindola,Majid Ezzati,Valery L. Feigin,Abraham D. Flaxman,Mohammad H. Forouzanfar,F.G.R. Fowkes,Richard C. Franklin,Marlene Fransen,Michael Freeman,Sherine E. Gabriel,Emmanuela Gakidou,Flavio Gaspari,Richard F. Gillum,Diego Gonzalez-Medina,Yara A. Halasa,Diana Haring,James Harrison,Rasmus Havmoeller,Rasmus Havmoeller,Roderick J. Hay,Bruno Hoen,Peter J. Hotez,Damian G Hoy,Kathryn H. Jacobsen,Spencer L. James,Rashmi Jasrasaria,Sudha Jayaraman,Nicole E. Johns,Ganesan Karthikeyan,Nicholas J Kassebaum,Andre Keren,Jon Paul Khoo,Lisa M. Knowlton,Olive Kobusingye,Adofo Koranteng,Rita Krishnamurthi,Michael S Lipnick,Steven E. Lipshultz,Summer Lockett Ohno,Jacqueline Mabweijano,Jacqueline Mabweijano,Michael F. Macintyre,Leslie Mallinger,Lyn March,Guy B. Marks,Robin Marks,Akira Matsumori,Richard Matzopoulos,Richard Matzopoulos,Bongani M. Mayosi,John H. McAnulty,Mary M. McDermott,John J. McGrath,Ziad A. Memish,George A. Mensah,Tony R. Merriman,Catherine Michaud,Matthew J. Miller,Ted R. Miller,Charles Mock,Ana Olga Mocumbi,Ali A. Mokdad,Andrew E. Moran,Kim Mulholland,M. Nathan Nair,Luigi Naldi,K.M. Venkat Narayan,Kiumarss Nasseri,Paul Norman,Martin O'Donnell,Saad B. Omer,Katrina F Ortblad,Richard H. Osborne,Doruk Ozgediz,Bishnu Pahari,Jeyaraj D Pandian,Andrea Panozo Rivero,Rogelio Perez Padilla,Fernando Perez-Ruiz,Norberto Perico,David Phillips,Kelsey Pierce,C. Arden Pope,Esteban Porrini,Farshad Pourmalek,Murugesan Raju,Dharani Ranganathan,Jürgen Rehm,David B. Rein,G. Remuzzi,Frederick P. Rivara,Thomas Roberts,Felipe Rodriguez De Leòn,Lisa C. Rosenfeld,Lesley Rushton,Ralph L. Sacco,Joshua A. Salomon,Uchechukwu Sampson,Ella Sanman,David C. Schwebel,Maria Segui-Gomez,Donald S. Shepard,David Singh,Jessica Singleton,Karen Sliwa,Emma Smith,Andrew C Steer,Jennifer A. Taylor,Bernadette Thomas,Imad M. Tleyjeh,Jeffrey A. Towbin,Thomas Truelsen,Eduardo A. Undurraga,Narayanaswamy Venketasubramanian,Lakshmi Vijayakumar,Theo Vos,Gregory R. Wagner,Mengru Wang,Wenzhi Wang,Kerrianne Watt,Martin A. Weinstock,Robert G. Weintraub,James D. Wilkinson,Anthony D. Woolf,Sarah Wulf,Pon Hsiu Yeh,Paul S. F. Yip,Azadeh Zabetian,Zhi Jie Zheng,Alan D. Lopez,Christopher J L Murray +195 more
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex, using the Cause of Death Ensemble model.
Journal ArticleDOI
Geodesic active contours
TL;DR: A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps.
Journal ArticleDOI
Geodesic Active Contours
TL;DR: In this article, a geodesic approach based on active contours evolving in time according to intrinsic geometric measures of the image is presented. But this approach is not suitable for 3D object segmentation.
Journal ArticleDOI
Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.
TL;DR: Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution.