Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography

Campos, Lúcio F. A.; Silva, A. C.; Barros, Allan Kardec

doi:10.1055/s-0038-1625409

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…Classification or class prediction is an ubiquitous task in clinical medicine. Some recent examples include the prediction of the presence of metastases in cancer patients [1], the differentiation between malignant, benign and normal tissue in women undergoing digital mammography [2], the identification of patients with a history of stroke [3], the prediction of prolonged hospital stay in an intensive care unit [4], or the detection of glaucoma [5], among others. Often, however, classification accuracy or predictive ability of the derived classification or prediction rules is not sufficiently large to justify their use in daily practice [1].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Evaluation of Boosting Methods for Classification

Stollhoff

Sauerbrei²,

Schumacher³

2010

Methods Inf Med

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Section: Introductionmentioning

confidence: 99%

An Experimental Evaluation of Boosting Methods for Classification

Stollhoff

Sauerbrei²,

Schumacher³

2010

Methods Inf Med

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“…The novelty described here shows the capacity of indicating the presence or absence of a cardiac disease. Yet, our methodology can be extended to other areas, such as detection of breast cancer [20], diabetes [21], and even distinguishing different modalities of motor imagery based on EEGs analysis [22]. Last but not least, our idea is also patients in remote areas, that is, where one does not have easy access to diagnosing tools.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

Lucena

Barros

Ohnishi

2016

BioMed Research International

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Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.

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“…Campos et al, [9] used independent component analysis (ICA) and neural network multilayer perceptron to classify mammograms as normal, benign and malignant, with 98.7% of successful.…”

Section: Introductionmentioning

confidence: 99%

Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images

RaghaDeepthiLoka¹,

Putheti²

2013

IJCA

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Content Based Image Retrieval systems are helpful to the radiologists in diagnosis of breast cancer. This paper presents a method for retrieving breast tissue as normal, benign or malignant in mammograms by using FuzzyTextons. In feature extraction first fuzzy texton images of mammograms are calculated. During the detection of fuzzy texton, fuzzy based quantization is performed to get more accurate textons. Then feature vectors are extracted for fuzzy textons and for efficient classification and retrieval Support Vector Machine is used. The proposed method was tested for a mammogram set from MIAS database.

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Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography

Abstract: The proposed method showed a good classification rate. The method will be useful for early cancer diagnosis.

Cited by 9 publications

References 6 publications

An Experimental Evaluation of Boosting Methods for Classification

An Experimental Evaluation of Boosting Methods for Classification

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images

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