Automated classification of common skin lesions using bioinspired features
Autor: | Miklós Gyöngy, K. Szalai, Domonkos Csabai |
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Rok vydání: | 2016 |
Předmět: |
education.field_of_study
medicine.medical_specialty business.industry Population Ultrasound Echogenicity 030218 nuclear medicine & medical imaging Lesion Support vector machine 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis medicine Computer vision Segmentation Radiology AdaBoost Artificial intelligence medicine.symptom Differential diagnosis education business |
Zdroj: | 2016 IEEE International Ultrasonics Symposium (IUS). |
DOI: | 10.1109/ultsym.2016.7728752 |
Popis: | Ultrasound (US) imaging of skin lesions provides information supplementary to dermoscopy and helps in improving diagnostic accuracy. The aim of the current work is to explore the feasibility of using ultrasound image features derived from radiological experience to distinguish between common skin lesions. 5–18 MHz B-mode ultrasound images were acquired of incoming patients. Images containing lesions 1–2 mm thick were selected (N=248), with histology used to diagnose suspicious lesions. 73 melanomas, 130 BCC, and 45 nevi were studied. Following semi-automatic segmentation, a number of relevant features expressing the geometry of the lesion boundary and boundary layer, as well as the image characteristics of the lesion, lesion boundary layer, and post-lesion region were considered. With the exception of lesion echogenicity, all features had an area under the curve (AUC) value of above 0.70. The AdaBoost and Support Vector Machine (SVM) classifiers were then trained and tested using cross-validation of 50 random equal populations of melanomas, BCC, and nevi; each population was then 2-fold (holdout) cross-validated 50 times. When detecting one group against two other groups, the detection of cancerous lesions fared best, with an AUC of at least 0.84 and a specificity of at least 19% at 100% sensitivity for both classifiers. The results demonstrate the potential of clinically useful ultrasound-based automatic differential diagnosis of skin lesions, which could perhaps be attained by better segmentation, having more training data, using several images of the same lesion when performing classification, as well as refinements in the definition of image features. |
Databáze: | OpenAIRE |
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