Computer aided breast cancer detection using ultrasound images

Pavithra, S.; Vanithamani, R.; Justin, Judith

doi:10.1016/j.matpr.2020.08.381

Cited by 24 publications

(20 citation statements)

References 11 publications

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“…The classifiers used are namely, SVM, RF, AdaBoost, gradient boosting etc. and the best performance is obtained by RF in [41] and [7], SVM in work [8] and [42]. This was one of the reasons behind using these two classifiers in our proposed framework.…”

Section: Performance Comparison With State-of-art Methodsmentioning

confidence: 93%

“…So, time required for tuning the parameters and training the model will be reduced. The work [7], [8], [41] uses a conventional machine learning approach for classification using the features obtained by CNN, gray-level co-occurrence matrix-based texture features, edgebased features, and morphological features. The classifiers used are namely, SVM, RF, AdaBoost, gradient boosting etc.…”

Section: Performance Comparison With State-of-art Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Breast Cancer Diagnosis Using Iterative Fourier-Bessel Decomposition Method Based CNN-kernel Features

Chaudhary¹,

Das²,

Pachori³

2022

Preprint

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Section: Performance Comparison With State-of-art Methodsmentioning

confidence: 93%

Section: Performance Comparison With State-of-art Methodsmentioning

confidence: 99%

Breast Cancer Diagnosis Using Iterative Fourier-Bessel Decomposition Method Based CNN-kernel Features

Chaudhary¹,

Das²,

Pachori³

2022

Preprint

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“…The use of manual feature extraction techniques in traditional machine learning-based CABTD methods [10,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] often requires domain expertise. In contrast to manual feature extraction techniques, the pre-trained DNN-based CABTD methods [16][17][18][37][38][39][40][41][42][43] extract the features automatically are effective and accurate when compared with traditional machine learning-based CABTD methods.…”

Section: Pre-trained Dnn-based Cabtd Methodsmentioning

confidence: 99%

“…In Ref. [41], the effectiveness of the CABTD method using finetuned inception v2 has been verified subjectively in association with three human readers. The computeraided diagnosis of breast cancer using an ensemble of DNNs such as VGG-like network, ResNet, and Dense Net is proposed [17].…”

Section: Pre-trained Dnn-based Cabtd Methodsmentioning

confidence: 99%

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Srikanth¹,

Krithiga²

2023

Intelligent Automation &Amp; Soft Computing

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Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors. However, a Brightness mode (B-mode) ultrasound image derives training feature samples that make closer isolation toward the infection part. Hence, it is expensive due to a meta-heuristic search of features occupying the global region of interest (ROI) structures of input images. Thus, it may lead to the high computational complexity of the pre-trained DNN-based CABTD method. This paper proposes a novel ensemble pre-trained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images. It conveys the additional consideration of a local-ROI-structures for further enhancing the pre-trained DNN-based CABTD method's breast tumor diagnostic performance without degrading its visual quality. The features are extracted at various depths (18, 50, and 101) from the global and local ROI structures and feed to support vector machine for better classification. From the experimental results, it has been observed that the combined local and global ROI structure of small depth residual network ResNet18 (0.8 in %) has produced significant improvement in pixel ratio as compared to ResNet50 (0.5 in %) and ResNet101 (0.3 in %), respectively. Subsequently, the pre-trained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors (Benign and Malignant) and improve the diagnostic accuracy (86%) compared to Dense Net, Alex Net, VGG Net, and Google Net. Moreover, it reduces the computational complexity due to the small depth residual network ResNet18, respectively.

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“…Fractal dimension (FD) is based on the concept of self-similarity. In image processing FD is defined as

where

is the least number of distinct copies of the 2D image in at the scale r [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors Algorithm

et al. 2022

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(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps; (ii) feature extraction, first order—standard deviation (SD), skewness (SK) and kurtosis (KR)—and second order—contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)—are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD); (iii) statistical analysis (t-test); (iv) classification with the K-Nearest Neighbors algorithm (K-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications.

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Computer aided breast cancer detection using ultrasound images

Cited by 24 publications

References 11 publications

Breast Cancer Diagnosis Using Iterative Fourier-Bessel Decomposition Method Based CNN-kernel Features

Breast Cancer Diagnosis Using Iterative Fourier-Bessel Decomposition Method Based CNN-kernel Features

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors Algorithm

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