Novel Preprocessing Technique in the Computer Aided Detection of Breast Cancer

Balasubramaniam, Dharini

doi:10.3844/jcssp.2012.1957.1960

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…In [16] the author proposed a MLRM (modified Local Range Modification) method of filtering the noise on mammogram images to predict the breast cancer which gives the 97.9% than the existing method LRM which is 93%.…”

Section: Existing Work 71 Feature Selection Algorithms On Medical Dmentioning

confidence: 99%

Analysis of Feature Selection Algorithms on Classification: A Survey

Vanaja¹,

Kumar²

2014

IJCA

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The aim of this paper is to discuss about various feature selection algorithms applied on different datasets to select the relevant features to classify data into binary and multi class in order to improve the accuracy of the classifier. Recent researches in medical diagnose uses the different kind of classification algorithms to diagnose the disease. For predicting the disease, the classification algorithm produces the result as binary class. When there is a multiclass dataset, the classification algorithm reduces the dataset into a binary class for simplification purpose by using any one of the data reduction methods and the algorithm is applied for prediction. When data reduction on original dataset is carried out, the quality of the data may degrade and the accuracy of an algorithm will get affected. To maintain the effectiveness of the data, the multiclass data must be treated with its original form without maximum reduction, and the algorithm can be applied on the dataset for producing maximum accuracy. Dataset with maximum number of attributes like thousands must incorporate the best feature selection algorithm for selecting the relevant features to reduce the space and time complexity. The performance of Classification algorithm is estimated by how accurately it predicts the individual class on particular dataset. The accuracy constrain mainly depends on the selection of appropriate features from the original dataset. The feature selection algorithms play an important role in classification for better performance. The feature selection is one of the preprocessing techniques in the classification. This research paper deals with different feature selection algorithms and their performance on different dataset. General TermsFeature selection algorithm, Medical dataset, Accuracy, Filter method, Wrapper method, embedded method.

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Section: Existing Work 71 Feature Selection Algorithms On Medical Dmentioning

confidence: 99%

Analysis of Feature Selection Algorithms on Classification: A Survey

Vanaja¹,

Kumar²

2014

IJCA

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Image Contrast Enhancement by Distances Among Points in Fuzzy Hyper-Cubes

Versaci

Calcagno

Morabito

2015

Lecture Notes in Computer Science

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Breast cancer detection using combined curvelet based enhancement and a novel segmentation methods

Senthilkumar¹,

Umamaheswari²

2015

BIOMED PAP

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Aim. This paper describes the digital implementation of a mathematical transform namely 2D Fast Discrete Curvelet Transform (FDCT) via UnequiSpaced Fast Fourier Transform (USFFT) in combination with the novel segmentation method for effective detection of breast cancer. Methods. USFFT performs exact reconstructions with high image clarity. Radon, ridgelet and Cartesian filters are included in this method. Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) were calculated for the image and the resulting value showed that the proposed method performs well on mammogram image in reducing noise with good extraction of edges. This work includes a novel segmentation method, which combines Modified Local Range Modification (MLRM) and Laplacian of Gaussian (LoG) edge detection method to segment the textured features in the mammogram image. Results. The result was analyzed using a Receiver Operating Characteristics (ROC) plot and the detection accuracy found was 99% which is good compared to existing methods.

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Novel Preprocessing Technique in the Computer Aided Detection of Breast Cancer

Cited by 3 publications

References 6 publications

Analysis of Feature Selection Algorithms on Classification: A Survey

Analysis of Feature Selection Algorithms on Classification: A Survey

Image Contrast Enhancement by Distances Among Points in Fuzzy Hyper-Cubes

Breast cancer detection using combined curvelet based enhancement and a novel segmentation methods

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