Feature subset selection for classification of malignant and benign breast masses in digital mammography

Chaieb, Ramzi; Kalti, Karim

doi:10.1007/s10044-018-0760-x

Cited by 21 publications

(16 citation statements)

References 21 publications

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“…Traditional CAD methods usually need to manually extract features from images [ 7 ]. These features include original features such as shape and texture [ 8 , 9 ], and the features extracted from the original features by machine learning algorithms, such as Histogram of Gradient [ 10 – 12 ], Local Binary Patter [ 13 , 14 ] and Gabor filter [ 11 , 12 ]. However, the selection and combination of features largely depend on the experience of designers, so the traditional methods have some limitations.…”

Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/ Malignancy) of two-view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.

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Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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“…It is an effective method for texture analysis, especially in biomedical images [ 20 ]. The extracted textured features, including contrast (CON), correlation (CORR), dissimilarity (DISS), angular second moment (ASM), entropy (ENT), and finally, the inverse different moment (IDM) of each feature, were extracted in 0°, 45°, 90°, and 135° directions [ 21 ]. The 36 colored features were extracted from the color-converted, enhanced image via a color moment approach.…”

Section: Methodsmentioning

confidence: 99%

Intelligent Diagnosis and Classification of Keratitis

et al. 2022

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A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual disability. Traditional clinical methods that use slit-lamp images can be tiresome, expensive, and time-consuming. Instead, this paper proposes a deep learning approach to diagnose corneal ulcers, enabling better, improved treatment. This paper suggests two modes to classify corneal images using manual and automatic deep learning feature extraction. Different dimensionality reduction techniques are utilized to uncover the most significant features that give the best results. Experimental results show that manual and automatic feature extraction techniques succeeded in discriminating ulcers from a general grading perspective, with ~93% accuracy using the 30 most significant features extracted using various dimensionality reduction techniques. On the other hand, automatic deep learning feature extraction discriminated severity grading with a higher accuracy than type grading regardless of the number of features used. To the best of our knowledge, this is the first report to ever attempt to distinguish corneal ulcers based on their grade grading, type grading, ulcer shape, and distribution. Identifying corneal ulcers at an early stage is a preventive measure that reduces aggravation and helps track the efficacy of adapted medical treatment, improving the general public health in remote, underserved areas.

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“…The concept was introduced by Claude Shannon and is called Shannon's entropy. The maximum, Renvi, Tsallis, spatial, minimum, conditional, cross, relative, and fuzzy entropies are used for image segmentation, image registration, image compression, image reconstruction, and edge detection in grey-level images [39]. Based on our investigation using only one of (mean, median, and entropy) will suffer from over-segmentation or under segmentation.…”

Section: ) New Threshold Techniquementioning

confidence: 96%

Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

et al. 2020

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Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this paper also included the groundtruth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.

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Feature subset selection for classification of malignant and benign breast masses in digital mammography

Cited by 21 publications

References 21 publications

New convolutional neural network model for screening and diagnosis of mammograms

New convolutional neural network model for screening and diagnosis of mammograms

Intelligent Diagnosis and Classification of Keratitis

Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

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