2022
DOI: 10.3390/tomography8050200
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Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

Abstract: Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning mod… Show more

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Cited by 26 publications
(17 citation statements)
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“…The results are frequently subpar when the first input is transmitted directly to the brain network. The ultrasound image is then further developed using the ACE calculation [65,66]. By calculating the general pixel value of the objective point and encompassing pixels by distinction, the ACE computation may be applied to the images.…”
Section: Ultrasound Detection Of Breast Nodulesmentioning
confidence: 99%
“…The results are frequently subpar when the first input is transmitted directly to the brain network. The ultrasound image is then further developed using the ACE calculation [65,66]. By calculating the general pixel value of the objective point and encompassing pixels by distinction, the ACE computation may be applied to the images.…”
Section: Ultrasound Detection Of Breast Nodulesmentioning
confidence: 99%
“…Compared with traditional radiomics, deep learning can directly use the original medical image as input, complete the learning of image features in the network, and directly use the subtyping result as the output. CNN is a classic deep learning network, especially widely used in image recognition and subtyping [11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, in developing CAD schemes of medical images, deep learning (DL) models have been well recognized and widely used to perform the tasks of segmenting the disease-infected regions of interest (ROIs) [ 7 , 8 ] and detecting or classifying diseases using the automatically extracted image features [ 9 , 10 ]. In using COVID-19 image datasets to develop CAD schemes, most of the previous studies focused on developing DL models to detect COVID-19 cases or classify between the COVID-19 and normal or other types of pneumonia cases [ 11 , 12 , 13 , 14 ].…”
Section: Introductionmentioning
confidence: 99%