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Author: search.filters.author.Pillay, NelendranSubject: search.filters.subject.Support vector machine

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    Comparative study of binary classifiers for reducing false negative detection of melanoma in skin lesions
    (IEEE, 2022-10-27) Jooravan, Amith; Reddy, Serendra; Pillay, Nelendran
    Reliable and accurate classification of a skin lesion is essential to the early diagnosis of skin cancer, especially melanoma. Traditional classification methods require performing a biopsy on the lesion. The overlap of benign and malignant clinical features may lead to incorrect melanoma diagnosis and/or excising an excessive number of benign lesions. This paper focuses on the use of machine learning to aid physicians with the non-invasive classification methodology of skin lesions, whilst prioritising the minimization of false negative classification. The clinical features used are based on the ABCD rule, representing the asymmetry, border, colour and diameter of the lesion. The dermoscopic images chosen are of melanoma lesions less than 0,76mm in thickness which corresponds to the early stages of cancer. The investigated classification methods include K-Nearest neighbours (KNN), Naïve Bayes and linear support vector machine. (LSVM). This research proposes the use of a LSVM machine learning algorithm to classify a skin lesion as being either melanoma or non-melanoma with the lowest false negative rate of the investigated classification. Classification accuracy of 85% and a false negative rate of 5% is achieved.
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    ANN’s vs. SVM’s for image classification
    (International ASET, 2012-08) Moorgas, Kevin Emanuel; Pillay, Nelendran; Governder, Poobalan
    In this paper the dynamic performance of the artificial neural network is compared to the performance of a statistical method such as the support vector machine. This comparison is made with respect to an image classification application where the performance is compared with regards to generalization and robustness. Image vectors are compressed in order to reduce the dimensionality and the salient feature vectors are extracted with the principle component algorithm. The artificial neural network and the support vector machine are trained to classify images with feature vectors. A comparative analysis is made between the artificial neural network and the support vector machine with respect to robustness and generalization.