A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study

Hamdi, Nezha; Auhmani, Khalid; Hassani, Moha M’Rabet

doi:10.11591/ijece.v5i5.pp1027-1034

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…However, the discovery of microcalsification is not enough to classify the breast cancer grades. Nezha H [7] classified breast cancer using the Quantum Clustering and Wavelet method. Shofwatul U [8] classified malignant and benign lesions using Feature Selection method.…”

Section: Introductionmentioning

confidence: 99%

Classification of breast cancer grades using physical parameters and K-nearest neighbor method

2019

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Breast cancer is a health problem in the world. To overcome this problem requires early detection of breast cancer. The purpose of this study is to classify early breast cancer grades. Combination of physical parameters with k-nearest neighbor Method is proposed to detect early breast cancer grades. The experiments were performed on 87 mammograms consisting of 12 mammograms of grade 1.41 mammograms of grade 2 and 34 mammogram of grade 3. The proposed method was effective to classify the grades of breast cancer by an accuracy of 64.36%, 50% sensitivity and 73.5% specitifity. Physical parameters can be used to classify grades of breast cancer. The results of this study can be used to complement the diagnosis of breast mammography examination.

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Section: Introductionmentioning

confidence: 99%

Classification of breast cancer grades using physical parameters and K-nearest neighbor method

2019

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“…In one class classification (Unary classification) problem, image of an object is classified as genuine object or an outlier object. This classification is useful in some data mining applications like outlier detection and anomaly detection [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Walsh Transform based Feature vector generation for Image Database Classification

Save¹

2016

IJECE

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Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques.

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“…TEXTURE analysis and categorization are important for the interpretation and understanding of real-world visual patterns. Texture classification has a wide variety of prospective applications [1] such as regions classification in satellite images [2], defects detection in industrial surface inspection [3], and classification of pulmonary disease [4], diagnosis of leukemic cells in medical image [5] and breast cancer classification [6]. Texture analysis and classification is majorly achieved in one of the two ways, i.e.…”

Section: Introductionmentioning

confidence: 99%

Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images

Kumar

Madhuri

et al. 2016

IJECE

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Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.

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A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study

Cited by 3 publications

References 21 publications

Classification of breast cancer grades using physical parameters and K-nearest neighbor method

Classification of breast cancer grades using physical parameters and K-nearest neighbor method

Walsh Transform based Feature vector generation for Image Database Classification

Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images

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