An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification

Abubacker, Nirase Fathima; Azman, Azreen; Doraisamy, Shyamala; Murad, Masrah Azrifah Azmi

doi:10.1007/s00521-016-2290-z

Cited by 26 publications

(16 citation statements)

References 41 publications

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“…Abubacker et al developed an efficient classification method using Genetic Association Rule Miner (GARM) and Neural Network (NN). In this literature paper, a multivariate filter was utilized for removing the inappropriate feature values, which helps to increase the classification efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Suresh

Rao

Reddy

2019

Concurrency and Computation

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Summary Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Suresh

Rao

Reddy

2019

Concurrency and Computation

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“…The comparison of public datasets based on origin, image size, image views, image format, image mode are presented in (Table 3). Commonly publicly available datasets are BCDR [58], MIAS [59], DDSM [60], Banco Web, mini-MIAS [72], WBC [75], IRMA [76], INbreast [77], BICBH and BreakHis used extensively and their distribution declared in (Figure 2b).…”

Section: Breast Cancer Digital Repositoriesmentioning

confidence: 99%

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Mahmood

Pei

et al. 2020

IEEE Access

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Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

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“…Singh et al [12] extended ENF for proposing neuro-fuzzy model. For every feature, less amount of linguistic variables are computed which makes the novelty of this method.…”

Section: Related Workmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

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Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

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An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification

Cited by 26 publications

References 41 publications

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Detection and classification of normal and abnormal patterns in mammograms using deep neural network

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

A Critical Review on Medical Image Processing Techniques

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