Comparison between extreme learning machine and wavelet neural networks in data classification

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Amar, Chokri Ben

doi:10.1117/12.2268648

Cited by 5 publications

(1 citation statement)

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“…The experimental evaluation of extracted features, i.e. classification methods, focus mainly on the statistical analysis of texture, and have been based on several classifiers such as multi-layer perceptron (MLP) networks, radial basis functions (RBFs), extreme learning machines (ELMs), wavelet neural networks, support vector machines (SVMs), the K-nearest-neighbor (KNN), or the adaptive fuzzy logic system (AFLS) (Kodogiannis et al 2007, Prasath 2016, Yahia et al 2017. MLP is a powerful 'expert' tool due to its remarkable ability to extract patterns and detect trends from imprecise data that are too complex to be noticed by either humans or other computer techniques.…”

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confidence: 99%

Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

Freitas¹,

Vieira²,

Lima³

et al. 2018

Phys. Med. Biol.

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Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

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confidence: 99%