Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network

Zhuang, Zhemin; Ding, Wanli; Zhuang, Shuxin; Raj, Alex Noel Joseph; Wang, Jinhong; Wang, Zhou; Wei, Chuliang

doi:10.1016/j.compmedimag.2021.101925

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…It involves crucial steps like tumor segmentation, feature extraction, and image preprocessing for enhancing classi cation accuracy. In another study Zhuang et al [24] used fully extracting Image of Interest (IOI) and Region of Interest (ROI) models to extract Areas of Interest (AOIs) from breast ultrasound images. They then used transfer learning combined with SDCB-NET and VGG to classify the extracted AOIs.…”

Section: Related Workmentioning

confidence: 99%

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Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Hamza,

Mezl

2024

Preprint

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Background Breast cancer remains a significant global health challenge, demanding accurate and effective diagnostic methods for timely treatment. Ultrasound imaging stands out as a valuable diagnostic tool for breast cancer due to its affordability, accessibility, and non-ionizing radiation properties. Methods We evaluate the proposed method using a publicly available breast ultrasound images. This paper introduces a novel approach to classifying breast ultrasound images based on segmentation and feature extraction algorithm. The proposed methodology involves several key steps. Firstly, breast ultrasound images undergo preprocessing to enhance image quality and eliminate potential noise. Subsequently, a U-Net + + is applied for the segmentation. A classification model is then trained and validated after extracting features by using Mobilenetv2 and Inceptionv3 of segmented images. This model utilizes modern machine learning and deep learning techniques to distinguish between malignant and benign breast masses. Classification performance is assessed using quantitative metrics, including recall, precision and accuracy. Our results demonstrate improved precision and consistency compared to classification approaches that do not incorporate segmentation and feature extraction. Feature extraction using InceptionV3 and MobileNetV2 showed high accuracy, with MobileNetV2 outperforming InceptionV3 across various classifiers. Results The ANN classifier, when used with MobileNetV2, demonstrated a significant increase in test accuracy (0.9658) compared to InceptionV3 (0.7280). In summary, our findings suggest that the integration of segmentation techniques and feature extraction has the potential to enhance classification algorithms for breast cancer ultrasound images. Conclusion This approach holds promise for supporting radiologists, enhancing diagnostic accuracy, and ultimately improving outcomes for breast cancer patients. In future our focus will be to use comprehensive datasets to validate our methodology.

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Section: Related Workmentioning

confidence: 99%

“…The feature vectors extracted from both InceptionV3 and MobileNetV2 are systematically stored [24] [31] [34]. These feature datasets collectively serve as the foundation for subsequent classi cation tasks and further analysis.…”

Section: Feature Extraction Models: Inceptionv3 and Mobilenetv2mentioning

confidence: 99%

Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Hamza,

Mezl

2024

Preprint

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“…Even though this model achieved a great result, the number of nonmass tumors used in this study was relatively small, which caused poor performance in diagnosing nonmass tumors. Zhuang et al [127] proposed a shallowly dilated convolutional branch network. This method not only had high accuracy but also greatly improved the speed and efficiency of breast tumor classification.…”

Section: Differentiation Of Benign and Malignant Breast Tumorsmentioning

confidence: 99%

Deep Learning on Ultrasound Imaging for Breast Cancer Diagnosis and Treatment: Current Applications and Future Perspectives

Wang¹,

Chen²,

Liu³

et al. 2023

Advanced Ultrasound in Diagnosis and Therapy

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Ultrasound is a commonly used imaging modality for breast cancer diagnosis and prognosis but suffers from false positives, false negatives and interobserver variability. Deep learning (DL), a subset of artificial intelligence, has the potential to improve the efficiency and accuracy of breast ultrasound. This article provides a comprehensive overview of DL applications for breast cancer diagnosis and treatment in ultrasound, including methodological descriptions of various DL models, and clinical applications on noise reduction, lesion localization, risk assessment, diagnosis, response evaluation and outcome prediction.Furthermore, the review highlights the importance of interpretability and small sample size learning of DL-based systems in clinical practice; specific recommendations for further expanding the clinical impact of DL-based systems are also provided.

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“…A disadvantage of ABUS is that obtaining a large number of images sharply increases the reading volume for ultrasound diagnostic physicians. To alleviate this burden, researchers are working to combine ABUS with arti cial intelligence for a number of applications, including the automatic identi cation of tumor lesions, tumor segmentation, tumor volume calculation, fully automated BI-RADS classi cation, and benign and malignant classi cation [18][19][20][21][22][23][24][25][26][27] . It has also been shown that the use of new deep learning networks combined with automatic segmentation networks for morphological analysis can help physicians to improve their accuracy in the diagnosis of breast cancer 28 .…”

Section: Introductionmentioning

confidence: 99%

A Nomogram for Diagnosis of BI-RADS 4 Breast Nodules Based on Three-dimensional Volume Ultrasound

Jiang

Chen

Yao

et al. 2023

Preprint

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Objective To investigate whether the use of a nomogram prediction model with automated breast ultrasound (ABUS) can help improve accuracy in the differentiation of benign and malignant Breast Imaging Reporting and Data System (BI-RADS) 4 breast nodules. Methods Data were collected for a total of 257 nodules with breast nodules corresponding to BI-RADS 4 who underwent ABUS examination and for whom pathology results were obtained from January 2019 to August 2022. The patients were divided into a benign group (188 cases) and a malignant group (69 cases) using a retrospective study method. Ultrasound imaging features were recorded. Logistic regression analysis was used to screen the clinical and ultrasound characteristics. Using the results of these analyses, a nomogram prediction model was established accordingly. Results Age, distance between nodule and nipple, calcification and C-plane convergence sign were independent risk factors that enabled differentiation between benign and malignant breast nodules (all P < 0.05). A nomogram model was established based on these variables. The AUC values for the nomogram model, age, distance between nodule and nipple, calcification, and C-plane convergence sign were 0.86, 0.735, 0.645, 0.697, and 0.685, respectively. Thus, the AUC value for the model was significantly higher than a single variable. Conclusions A nomogram based on the clinical and ultrasound imaging features of ABUS can be used to improve the accuracy of the diagnosis of benign and malignant BI-RADS 4 nodules. It can function as a relatively accurate predictive tool for sonographers and clinicians and is therefore clinically useful. Advances in knowledge statement: we retrospectively analyzed the clinical and ultrasound characteristics of ABUS BI-RADS 4 nodules and established a nomogram model to improve the efficiency of the majority of ABUS readers in the diagnosis of BI-RADS 4 nodules.

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Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network

Cited by 16 publications

References 21 publications

Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Deep Learning on Ultrasound Imaging for Breast Cancer Diagnosis and Treatment: Current Applications and Future Perspectives

A Nomogram for Diagnosis of BI-RADS 4 Breast Nodules Based on Three-dimensional Volume Ultrasound

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