Classification of Malignant Melanoma and Benign Nevi from Skin Lesions Based on Support Vector Machine

2013 ◽  
Author(s):  
Mohamed Khalad Abu Mahmoud ◽  
Adel Al-Jumaily ◽  
Yashar Maali ◽  
Khairul Anam
Keyword(s):  
Support Vector Machine ◽  
Malignant Melanoma ◽  
Skin Lesions ◽  
Support Vector ◽  
Benign Nevi

scholarly journals Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Joanna Jaworek-Korjakowska
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Early Stage ◽  
Skin Lesions ◽  
Support Vector ◽  
Svm Classifier ◽  
Stage Of Development ◽  
Vector Machines ◽  
Aided Diagnosis

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification.Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs.Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination.Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

scholarly journals Diagnostic Performance of a Support Vector Machine for Dermatofluoroscopic Melanoma Recognition: The Results of the Retrospective Clinical Study on 214 Pigmented Skin Lesions

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 103 ◽  
Author(s):  
Łukasz Szyc ◽  
Uwe Hillen ◽  
Constantin Scharlach ◽  
Friederike Kauer ◽  
Claus Garbe
Keyword(s):  
Support Vector Machine ◽  
Malignant Melanoma ◽  
Fluorescence Spectra ◽  
Learning Algorithm ◽  
Skin Lesions ◽  
Support Vector ◽  
Retrospective Clinical Study ◽  
Scoring Algorithm ◽  
Pigmented Skin Lesions

The need for diagnosing malignant melanoma in its earliest stages results in an increasing number of unnecessary excisions. Objective criteria beyond the visual inspection are needed to distinguish between benign and malignant melanocytic tumors in vivo. Fluorescence spectra collected during the prospective, multicenter observational study (“FLIMMA”) were retrospectively analyzed by the newly developed machine learning algorithm. The formalin-fixed paraffin-embedded (FFPE) tissue samples of 214 pigmented skin lesions (PSLs) from 144 patients were examined by two independent pathologists in addition to the first diagnosis from the FLIMMA study, resulting in three histopathological results per sample. The support vector machine classifier was trained on 17,918 fluorescence spectra from 49 lesions labeled as malignant (1) and benign (0) by three histopathologists. A scoring system that scales linearly with the number of the “malignant spectra” was designed to classify the lesion as malignant melanoma (score > 28) or non-melanoma (score ≤ 28). Finally, the scoring algorithm was validated on 165 lesions to ensure model prediction power and to estimate the diagnostic accuracy of dermatofluoroscopy in melanoma detection. The scoring algorithm revealed a sensitivity of 91.7% and a specificity of 83.0% in diagnosing malignant melanoma. Using additionally the image segmentation for normalization of lesions’ region of interest, a further improvement of sensitivity of 95.8% was achieved, with a corresponding specificity of 80.9%.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification
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