Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

Islam, Warid; Jones, Meredith; Faiz, Rowzat; Sadeghipour, Negar; Qiu, Yuchen

doi:10.3390/tomography8050200

Cited by 24 publications

(17 citation statements)

References 40 publications

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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%

An Outlook of Medical Image Analysis via Transfer Learning Approaches

Nagaraju¹,

Rao²

2022

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Artificial intelligence advancements, particularly deep learning algorithms, are helpful for identifying, classifying, and rating designs in clinical images. Clinical diagnosis and scientific research both rely heavily on medical image analysis. The diagnosis of medical conditions frequently involves medical image acquisition techniques like pathology, computed tomography, magnetic resonance imaging, ultrasound, and x-ray. Transfer learning stands out from other deep learning techniques thanks to its simplicity, effectiveness, affordable training costs, and capacity to escape the dataset curse. The diagnosis of anomalies including Alzheimer's disease, diabetic retinopathy, colon cancer, breast cancer, and pulmonary nodule can be made using the medical imaging techniques combined with datasets and computer vision. These approaches are helpful in non-invasive qualitative and quantitative analysis on patients. However, labelling in medical images are still scarce. This paper mainly reviews the application of transfer learning in medical image analysis. Beginners can benefit from this review paper's guidance as they gain a thorough and organized understanding of transfer learning applications for the analysis of medical images. By establishing laws that will aid future advancements in medical image processing, policymakers in adjacent sectors will also profit from the trend of transfer learning in medical imaging.

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Section: Ultrasound Detection Of Breast Nodulesmentioning

confidence: 99%

An Outlook of Medical Image Analysis via Transfer Learning Approaches

Nagaraju¹,

Rao²

2022

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“…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%

Ischemic stroke subtyping method combining convolutional neural network and radiomics

Chen

Jiang

et al. 2023

XST

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BACKGROUND: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics. METHODS: Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model. RESULTS: The experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People’s Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively. CONCLUSION: The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients.

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“…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%

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists

et al. 2023

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Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

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Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

Cited by 24 publications

References 40 publications

An Outlook of Medical Image Analysis via Transfer Learning Approaches

An Outlook of Medical Image Analysis via Transfer Learning Approaches

Ischemic stroke subtyping method combining convolutional neural network and radiomics

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists

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