Fourier-based shape feature extraction technique for computer-aided B-Mode ultrasound diagnosis of breast tumor

Lee, Jong-Ha; Seong, Yeong Kyeong; Chang, Chu-Ho; Park, Jinman; Park, Moonho; Woo, Kyoung-Gu; Ko, Eun Young

doi:10.1109/embc.2012.6347495

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Computer-aided diagnosis (CAD) systems have been proposed to assist radiologists in the interpretation of breast US exams over a decade ago 18 . Early CAD systems often relied on handcrafted visual features that are difficult to generalize across US images that were acquired using different protocols and US units [19][20][21][22][23][24] . Recent advances in deep learning have facilitated the development of AI systems for the automated diagnosis of breast cancer from US images [25][26][27] .…”

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confidence: 99%

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

et al. 2021

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Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.

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confidence: 99%

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

et al. 2021

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“…Lee et al[17] studied a novel Fourier-based shape feature extraction technique and proved that this technique provides higher classification accuracy for breast tumors in computer-aided B-mode ultrasound diagnosis system.…”

Section: Classification and Recognitionmentioning

confidence: 99%

Artificial intelligence in breast ultrasound

Wu¹,

Zhou²,

Xu³

et al. 2019

WJR

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Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

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“…To aid radiologists in evaluating breast ultrasound testing, computer-aided diagnosis (CAD) systems were created [ 15 , 16 ]. It is challenging to generalize the visual information used by older CAD systems [ 17 , 18 , 19 , 20 , 21 , 22 ] to ultrasound images captured using various techniques. AI methods for automated breast cancer detection utilizing ultrasound images have made great strides forward in recent years [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

et al. 2023

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According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.

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Fourier-based shape feature extraction technique for computer-aided B-Mode ultrasound diagnosis of breast tumor

Cited by 5 publications

References 14 publications

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Artificial intelligence in breast ultrasound

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

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