A Methodological Approach to the Classification of Dermoscopy Images
M. Emre Celebi,Hassan A. Kingravi,Bakhtiyar Uddin,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss +6 more
TLDR
A methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented and the issue of class imbalance is addressed using various sampling strategies and the classifier generalization error is estimated using Monte Carlo cross validation.About:
This article is published in Computerized Medical Imaging and Graphics.The article was published on 2007-09-01 and is currently open access. It has received 583 citations till now. The article focuses on the topics: Feature selection & Support vector machine.read more
Citations
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Journal ArticleDOI
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
TL;DR: A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.
Journal ArticleDOI
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
TL;DR: This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.
Proceedings ArticleDOI
PH 2 - A dermoscopic image database for research and benchmarking
TL;DR: The PH2 database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermosCopic images.
Journal ArticleDOI
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance
Yading Yuan,Ming Chao,Yeh-Chi Lo +2 more
TL;DR: A fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data to ensure effective and efficient learning with limited training data is presented.
Journal ArticleDOI
Deep learning ensembles for melanoma recognition in dermoscopy images
Noel C. F. Codella,Quoc-Bao Nguyen,Sharathchandra U. Pankanti,David A. Gutman,Brian Helba,Allan C. Halpern,John R. Smith +6 more
TL;DR: A system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection is proposed.
References
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Statistical learning theory
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C4.5: Programs for Machine Learning
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
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