Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

Nemat, Hoda; Fehri, Hamid; Ahmadinejad, Nasrin; Frangi, Alejandro F.; Gooya, Ali

doi:10.1002/mp.13082

Cited by 28 publications

(7 citation statements)

References 70 publications

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“…The resulting classification attained an accuracy of 91.11%. (Nemat et al, 2018) proposed the utilization of a computer-aided diagnostic system (CAD) that incorporates a preprocessing operation to improve the quality of ultrasound depicting breast cancers. Subsequently, the use of the watershed method was employed for the purpose of segmenting the breast tumor.…”

Section: Classical Methodsmentioning

confidence: 99%

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

He,

Diao,

et al. 2024

Image Anal Stereol

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Breast cancer is a prominent contributor to mortality associated with cancer in the female population on a global scale. The timely identification and precise categorization of breast cancer are of utmost importance in enhancing patient prognosis. Nevertheless, the task of precisely categorizing breast cancer based on ultrasound imaging continues to present difficulties, primarily due to the presence of dense breast tissues and their inherent heterogeneity. This study presents a unique approach for breast cancer categorization utilizing the wavelet based vision transformer network. To enhance the neural network’s receptive fields, we have incorporated the discrete wavelet transform (DWT) into the network input. This technique enables the capture of significant features in the frequency domain. The proposed model exhibits the capability to effectively capture intricate characteristics of breast tissue, hence enabling correct classification of breast cancer with a notable degree of precision and efficiency. We utilized two breast tumor ultrasound datasets, including 780 cases from Baheya hospital in Egypt and 267 patients from the UDIAT Diagnostic Centre of Sabadell in Spain. The findings of our study indicate that the proposed transformer network achieves exceptional performance in breast cancerclassification. With an AUC rate of 0.984 and 0.968 on both datasets, our approach surpasses conventional deep learning techniques, establishing itself as the leading method in this domain. This study signifies a noteworthy advancement in the diagnosis and categorization of breast cancer, showcasing the potential of the proposed transformer networks to enhance the efficacy of medical imaging analysis.

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Section: Classical Methodsmentioning

confidence: 99%

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

He,

Diao,

et al. 2024

Image Anal Stereol

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“…The shape-based features very effectively differentiated various breast lesions in some researches [18,19] and showed that these features are more appropriate for breast tumor detection. The objective of shape-based features is to measure contour and shape characteristics of breast lesions.…”

Section: Introductionmentioning

confidence: 99%

“…In CADx systems, the main focus is the automatic finding and classification of breast lesion. Imaging modalities supporting the type of texture features [14][15][16] and shape-based features [18][19][20] have been employed to identify breast cancer ailment. However, it is still a difficult job to choose appropriate features for finding cancer at its early stage [19].…”

Section: Introductionmentioning

confidence: 99%

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

et al. 2020

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Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method.

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“…Results for classification of the 57 extracted texture and shape features giving the highest classification accuracy of 84.6%. Another literature [5] proposed a watershed method for semi-automatic tumor segmentation. After extracting a set of 855 features including shape or texture from each tumor area, a Bayesian Automatic Relevance Detection (ARD) was used to reduce the feature and dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

Tian

et al. 2021

BioMed Eng OnLine

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Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. Results The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

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Classification of breast lesions in ultrasonography using sparse logistic regression and morphology‐based texture features

Abstract: Using ARD, the proposed CAD system selects five new features for breast tumor classification and outperforms state-of-the-art, making a reliable and complementary tool to help clinicians diagnose breast cancer.

Cited by 28 publications

References 70 publications

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

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