Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model

Yang

Computational Intelligence and Neuroscience

et al. 2021

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

“…The following measures (equations ( 15 )–( 19 )) were used as a metric to evaluate the performance of the SVM classifier model [ 35 ].

Section: Resultsmentioning

confidence: 99%

“…e following measures (equations ( 15)-( 19)) were used as a metric to evaluate the performance of the SVM classifier model [35].…”

Section: Evaluation Indexesmentioning

confidence: 99%

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Yang

Computational Intelligence and Neuroscience

et al. 2021

“…Luminal and Liao et al [56] introduced a combination feature model of B-mode ultrasound image and strain elastography and showed better performance than that…”

Section: Diagnostic Support By Deep Learning Analyticsmentioning

confidence: 99%

“…A c c e p t e d A r t i c l eaccuracy by using various information such as Doppler and elastography other than B-mode[56,57].…”

mentioning

confidence: 99%

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Kang

Kwon

et al. 2021

Ultrasonography

Reviews of the most recent applications of deep learning on ultrasound imaging applications are presented. Architectures of deep learning networks are briefly explained for medical imaging application categories of classification, detection, segmentation, and generation. Ultrasonography applications are then reviewed and summarized for image processing and diagnosis along with some representative study cases of breast, thyroid, heart, kidney, liver, and fetal head. Efforts on workflow enhancement are also reviewed with emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented on image quality Enhancement, diagnostic support, and improving workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.

“…ABUS opens promising scenarios in the field of artificial intelligence to be confirmed with other studies. Future perspectives include the integration of radiomics and deep learning in the further development of 3D ABUS [51].…”

Section: Artificial Intelligencementioning

confidence: 99%

Automatic breast ultrasound: state of the art and future perspectives

Nicosia¹,

Ferrari²,

Bozzini³

et al. 2020

ecancer

The three-dimensional automated breast ultrasound system (3D ABUS) is a new device which represents a huge innovation in the breast ultrasound field, with several application scenarios of great interest. ABUS's aim is to solve some of the main defects of traditional ultrasound, such as lack of standardization, high level of skill non-reproducibility, small field of view and high commitment of physician time. ABUS has proven to be an excellent non-ionising alternative to other supplemental screening options for women with dense breast tissue; also, it has appeared to be very promising in daily clinical practice. The purpose of this paper is to present a summary of current applications of ABUS, focusing on clinical applications and future perspectives as ABUS is particularly promising for studies involving artificial intelligence, radiomics and evaluation of breast molecular subtypes.