CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features

Zemmal, Nawel; Azizi, Nabiha; Sellami, Mokhtar

doi:10.1109/isps.2015.7244993

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…Because of the medical significance of screening breast cancer, there has been considerable effort on developing CAD approaches for detecting abnormalities, including calcifications, masses, architectural distortion and bilateral asymmetry [28] [15] [11] [32]. Traditional CAD approaches rely on manually designed image features [28] [15] in detecting subtle yet crucial abnormalities in mammograms.…”

Section: Introductionmentioning

confidence: 99%

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Shu

Goubran

2018

2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

102

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Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.

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Section: Introductionmentioning

confidence: 99%

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Shu

Goubran

2018

2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

102

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“…The primary aim of the CAD system is to diagnose the suspicious area of the breast and mark the regions of interests which can be lesions. N. Zemmal et al [43] and D. Saraswathi et al [44] revealed that CAD detection systems have enhanced the accuracy of the radiologist for the detection of breast cancer. This section discusses the methodologies for the screening of breast cancer through CAD.…”

Section: B Cad System For Breast Cancer Diagnosesmentioning

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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“…A tool for the diagnosis of mammography abnormalities is proposed in [20]. The key idea is to depend on semi-supervised classification using a transductive support vector machine (TSVM) with different kernel functions and heterogeneous feature families.…”

Section: B Taxonomy Of Ai-based Approaches For Breast Cancer Diagnosismentioning

confidence: 99%

“…From a medical point of view, the early detection of breast cancer contributes to saving the lives of patients as well as decreasing the cost of treatment at both the private and governmental medical institution levels. Computer science researchers have employed AI for this purpose, and many approaches have been proposed, such as [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. However, the quality of any proposed approach for breast cancer detection is evaluated based on its accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Alfifi¹,

Alrahhal²,

Bataineh³

et al. 2020

IJACSA

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Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics.

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CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features

Cited by 13 publications

References 39 publications

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

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

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

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