Nezha Hamdi

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In this paper we present a high accuracy computer-aided diagnosis scheme. The goal of the developed system is to classify benign and malignant microcalcifications on mammograms. It is mainly based on a combination of wavelet decomposition, feature extraction and classification methodology using Fisherpsilas linear discriminant. The contribution of wavelet(More)
In this paper, we present a comparative study of dimension reduction methods combined with wavelet transform. This study is carried out for mammographic image classification. It is performed in three stages: extraction of features characterizing the tissue areas then a dimension reduction was achieved by four different methods of discrimination and finally(More)
This paper presents a study of feature selection methods effect, using a filter approach, on the accuracy and error of supervised classification of cancer. A comparative evaluation between different selection methods: Fisher, T-Statistics, SNR and ReliefF, is carried out, using the dataset of different cancers; leukemia cancer, prostate cancer and colon(More)
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum Clustering for Feature Selection performs the selection in two steps. Partitioning the original features space in order to group similar features is performed using the Quantum Clustering algorithm. Then the selection of a representative for each cluster is(More)
This paper presents a comparative study of dimension reduction methods combined with wavelet transform. This study is carried out for mammographic image classification. It is performed in three stages: extraction of features characterizing the tissue areas then a dimension reduction was achieved by four different methods of discrimination and finally the(More)
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