A novel method for peanut variety identification and classification by Improved VGG16

Yang, Haoyan; Ni, Jiangong; Gao, Jiyue; Han, Zhongzhi; Luan, Tao

doi:10.1038/s41598-021-95240-y

Cited by 77 publications

(36 citation statements)

References 26 publications

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“…VGG-16 was used to extract deep-learning features ( 23 , 24 ). The maximum cross-sectional area of the tumor ROI was selected and cropped to the two-dimensional rectangular image covering the entire tumor.…”

Section: Methodsmentioning

confidence: 99%

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

Wang

Hu²,

Zhan³

et al. 2022

Front. Oncol.

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PurposeTo develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer.MethodsA total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors.ResultsFive radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature.ConclusionsBoth radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.

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Section: Methodsmentioning

confidence: 99%

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

Wang

Hu²,

Zhan³

et al. 2022

Front. Oncol.

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“…The oil tea cultivar identification dataset was collected using a smartphone and then calibrated by human experts. (3) Based on the oil tea cultivar identification dataset, extensive comparative experiments were conducted on an EfficientNet-B4-CBAM against VGG16 [41], InceptionV3 [42], ResNet50 [43], EfficientNet-B4 [44], and EfficientNet-B4-SE [45]. The results show that the proposed EfficientNet-B4-CBAM is superior to the other methods in comparative experiments, proving the effectiveness of embedding a CBAM module.…”

Section: Introductionmentioning

confidence: 93%

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Zhu

Zhang

Sun

et al. 2021

Forests

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Cultivar identification is a basic task in oil tea (Camellia oleifera C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.

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“…VGG16 improves on the standard AlexNet by using numerous 33 convolution cores to substitute the larger convolution cores (1111,707,55), which can extend the depth of the network and minimize the number of network parameters (Yang et al, 2021). There are 3 parts to VGG16 transfer learning such as convolution, pooling, and fully connected layers.…”

Section: Deep Feature Extractionmentioning

confidence: 99%

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action.Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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A novel method for peanut variety identification and classification by Improved VGG16

Cited by 77 publications

References 26 publications

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

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