A new detection model of microaneurysms based on improved FC-DenseNet

Wang, Zhenhua; Li, Xiaokai; Yao, Mengfei; Li, Jing; Jiang, Qing; Yan, Biao

doi:10.1038/s41598-021-04750-2

Cited by 12 publications

(6 citation statements)

References 33 publications

(25 reference statements)

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“…To compare the performance of the DTL models, the following five performance indices were calculated as the metrics in this study: accuracy (Ac), precision (Pr), recall rate (Rc), F1 score (f1) and area under the receiver operating characteristic (ROC) curve (AUC) [ 21 ]. The positive and negative cases were assigned to the malignant and benign groups, respectively.…”

Section: Methodsmentioning

confidence: 99%

Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet

Wang

Zhang

et al. 2023

Diagnostics

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It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI).

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Section: Methodsmentioning

confidence: 99%

Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet

Wang

Zhang

et al. 2023

Diagnostics

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“…To restore the input resolution, FC-DenseNet [15] offers an upsampling path. FC-DenseNet is made up of dense blocks and transition layers.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

Ramírez-Asís

Bolivar

Gonzales

et al. 2022

Computational Intelligence and Neuroscience

146

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Most approaches use interactive priors to find tumours and then segment them based on tumour-centric candidates. A fully convolutional network is demonstrated for end-to-end breast tumour segmentation. When confronted with such a variety of options, to enhance tumour detection in digital mammograms, one uses multiscale picture information. Enhanced segmentation precision. The sampling of convolution layers are carefully chosen without adding parameters to prevent overfitting. The loss function is tuned to the tumor pixel fraction during training. Several studies have shown that the recommended method is effective. Tumour segmentation is automated for a variety of tumour sizes and forms postprocessing. Due to an increase in malignant cases, fundamental IoT malignant detection and family categorisation methodologies have been put to the test. In this paper, a novel malignant detection and family categorisation model based on the improved stochastic channel attention of convolutional neural networks (CNNs) is presented. The lightweight deep learning model complies with tougher execution, training, and energy limits in practice. The improved stochastic channel attention and DenseNet models are employed to identify malignant cells, followed by family classification. On our datasets, the proposed model detects malignant cells with 99.3 percent accuracy and family categorisation with 98.5 percent accuracy. The model can detect and classify malignancy.

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“…To compare the performance of DTL models, the following ve performance indices were calculated: accuracy (Ac), precision (Pr), recall rate(Rc), F1 score (f1), and area under the receiver operating characteristic (ROC) curve (AUC) as the metrics in this study [20]. The positive and negative cases were assigned to the malignant and benign groups, respectively.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Classification of breast lesions on DCE-MRI Data Using Fine-tuned MobileNet

Meng

Zhang

et al. 2022

Preprint

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Background Early and rapid diagnosis of breast cancer is very important. Traditional method for detecting and diagnosing breast cancer may lead to a false positive or negative result. Presently, most of the deep learning models used in breast cancer detection prevents their use on mobile phones or low-configuration devices. This study intends to evaluate the capability of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods The diagnostic efficiencies of MobileNetV1_False(V1_False), MobileNetV1_True (V1_True), MobileNetV2_False(V2_False) and MobileNetV2_True(V2_True) were tested and evaluated under the same program architecture and dataset (2124 images in benign group and 2226 images in malignant group). A relatively optimal model was selected (with the highest accuracy in the training set and test set), and two fine-tuning strategies (S0 and S1) were used to modify it. The accuracy (Ac) of different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), f1 score(f1) and area under the receiver operating characteristic curve (AUC), were taken as the performance indicators. Results The Ac of V1_True (0.9815) was higher than those of V1_False (0.9749), V2_False (0.9672) and V2_True (0.9699). Overfitting were observed in all models. Pr, Rc, f1 and AUC of V1_True were 0.79,0.73,0.74 and 0.74, respectively, they were higher than those of V1_False (0.77,0.73,0.73 and 0.73), V2_False (0.74,0.65,0.66 and 0.65) and V2_True (0.76,0.67,0.68 and 0.67). Conclusion The MobileNetV1_True model can differentiate between benign and malignant breast lesions on breast DCE-MRI, and has the potential to be deployed on an embedded mobile platform. Future work is necessary to improve the generalization capability of the proposed method.

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A new detection model of microaneurysms based on improved FC-DenseNet

Cited by 12 publications

References 33 publications

Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet

Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet

A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

Classification of breast lesions on DCE-MRI Data Using Fine-tuned MobileNet

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