Name
Abstract | ||
|---|---|---|
Diagnosis of benign and malign skin lesions is currently mostly relying on visual assessment and frequent biopsies performed by dermatologists. As the timely and correct diagnosis of these skin lesions is one of the most important factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. In this paper we propose a machine learning approach to classify melanocytic lesions into malignant and benign from dermoscopic images. The dermoscopic image database is composed of 4240 benign lesions and 232 malignant melanoma. For segmentation we are using multiphase soft segmentation with total variation and H1 regularization. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters that try to reflect structures used in medical diagnosis. The learning and classification stage is performed using SVM with polynomial kernels. The classification delivered accuracy of 98.57% with a true positive rate of 0.991% and a false positive rate of 0.019%. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-28460-1_10 | MCBR-CDS |
Keywords | Field | DocType |
multiphase soft segmentation,malign skin lesion,dermoscopic image,malignant melanoma,benign lesion,false positive rate,dermoscopic image database,correct diagnosis,pigmented skin dermoscopic image,classification stage,medical diagnosis,machine learning,classification,supervised learning | False positive rate,Feature vector,Pigmented skin,Pattern recognition,Segmentation,Computer-aided diagnosis,Support vector machine,Supervised learning,Artificial intelligence,Medicine,Medical diagnosis | Conference |
Citations | PageRank | References |
5 | 0.44 | 8 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asad Safi | 1 | 7 | 2.17 |
| Maximilian Baust | 2 | 155 | 19.15 |
| Olivier Pauly | 3 | 154 | 13.13 |
| Victor Castaneda | 4 | 41 | 3.17 |
| Tobias Lasser | 5 | 97 | 16.81 |
| Diana Mateus | 6 | 417 | 32.74 |
| Nassir Navab | 7 | 6594 | 578.60 |
| Rüdliger Hein | 8 | 5 | 0.44 |
| Mahzad Ziai | 9 | 6 | 0.79 |