Breast cancer detection using rank nearest neighbor classification rules

Bagui, Sikha; Pal, Krishan; Pal, Nikhil R.

doi:10.1016/s0031-3203(02)00044-4

Cited by 85 publications

(33 citation statements)

References 15 publications

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“…The k-NN algorithm achieved the accuracy of 98.25% in the testing set, and fuzzy k-NN acquired 98.83%. In 2003, rank k-NN has been used to achieve 98.10% [105]. In 2013, four methods of calculating distance were used for k-NN to determine the distance between each WBCD of the data points [106].…”

Section: Dtsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Section: Dtsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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“…The technique used here and the classification rate of 97.6% proves highly competitive with other algorithms, for example [9] uses a generalized rank nearest neighbour rule and achieves a classification rate of 96.2%. Nauk and Kruse [10] use a fuzzy classification method, with a best classification rate of 96.7%.…”

Section: Experimental Results For the Wbcdmentioning

confidence: 94%

“…not combining, the missing attribute values of the corresponding data items. This is an advantage over other approaches, for example [9] and [10], which would have to exclude the 16 items with missing values. In addition, with the D-S theory it is possible to attempt to classify data items based on single attributes, combinations of attributes or using all attribute values, and hence determine which combinations work best.…”

Section: Description Of the Datamentioning

confidence: 99%

Data classification using the Dempster–Shafer method

Chen

Whitbrook

Aickelin

et al. 2014

Journal of Experimental & Theoretical Artificial Intelligen

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In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets that have two, three and four classes. In each case, a subset of the available data is used for training to establish thresholds, limits or likelihoods of class membership for each attribute, and hence create mass functions that establish probability of class membership for each attribute of the test data. Classification of each data item is achieved by combination of these probabilities via Dempster's Rule of Combination. Results for the first two datasets show extremely high classification accuracy that is competitive with other popular methods. The third dataset is non-numerical and difficult to classify, but good results can be achieved provided the system and mass functions are designed carefully and the right attributes are chosen for combination. In all cases the Dempster-Shafer method provides comparable performance to other more popular algorithms, but the overhead of generating accurate mass functions increases the complexity with the addition of new attributes. Overall, the results suggest that the D-S approach provides a suitable framework for the design of classification systems and that automating the mass function design and calculation would increase the viability of the algorithm for complex classification problems.

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“…They investigated and analyzed 14 feature extraction techniques with neural network classification. Bagui et al [4] proposed a new generalization of the rank nearest neighbor (RNN) rule for the diagnosis of breast cancer from multivariate data. Cheng et al [9] summarized enhancement and segmentation algorithms, mammographic features, and classifiers used in various stages of the CAD and compared their performances.…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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Breast cancer detection using rank nearest neighbor classification rules

Cited by 85 publications

References 15 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Data classification using the Dempster–Shafer method

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

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