Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions

Capdehourat, Germán; Corez, Andrés; Bazzano, Anabella; Alonso, Rodrigo; Musé, Pablo

doi:10.1016/j.patrec.2011.06.015

Cited by 98 publications

(61 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial neural network ensemble [1], Adaboost [15], ANN [11] and KNN [13] have been widely used for lesion classification. Many factors influence classification performance, such as parameter settings, extracted features or feature combinations, and the quality of the experimental samples.…”

Section: Classification Using Svm Classifiermentioning

confidence: 99%

Classification of Melanoma on Dermoscopy Images using SVM Classifier

M¹

2018

IJRASET

View full text Add to dashboard Cite

By analyzing the dermoscopy pictures, a unique technique is developed for classifying melanocytic tumors as benign or malignant. The algorithm contains the following steps: first, lesions extracted using k-means method; then second, features are extracted; and third, lesion classification by using a classifier based on a Support Vector Machine (SVM) model. Lesions occur that are overlarge to be entirely contained among the dermoscopy image. To influence this tough presentation, new features are proposed, and which are able to characterize border irregularities on both complete and incomplete lesions. In this model, to achieve improved performance a SVM classifier is intended. Experiments are administered on two numerous dermoscopy databases that embrace images of both the xanthous and caucasian races. The results show that classification accuracy is greatly enhanced by the employment of the new features and also the proposed classifier model.

show abstract

Section: Classification Using Svm Classifiermentioning

confidence: 99%

Classification of Melanoma on Dermoscopy Images using SVM Classifier

M¹

2018

IJRASET

View full text Add to dashboard Cite

show abstract

“…Artificial neural network ensemble [1], Adaboost [16] and KNN [14] have been widely used for lesion classification. Many factors influence classification performance, such as parameter settings, extracted features or feature combinations, and the quality of the experimental samples.…”

Section: Classification Using Svm Classifiermentioning

confidence: 99%

Effective Classification Techniques for Melanoma Classification on Dermoscopy Images

Ashtami¹

2018

IJRASET

View full text Add to dashboard Cite

Dermoscopy technique is one of the major imaging modalities used in the diagnosis of the pigmented skin lesions. Computerized image analysis techniques have become important tools in this research area due to the difficulty and subjectivity of human interpretation. The proposed system includes three steps: firstly, preprocess the input image secondly, segmentation and then classification. Classifying the melanocytic tumors as benign or malignant by the analysis of dermoscopy images using Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifier.

show abstract

“…In [80], the authors use an automatic hair removal algorithm which consists of hair detection and image inpainting. The hair removal algorithm [70] used was previously here described.…”

Section: Hair Removalmentioning

confidence: 99%