The early identification and classification of growth disturbances of the proximal end of the femur.

1986 ◽  
Vol 68 (7) ◽  
pp. 970-980 ◽  
Author(s):  
T OʼBrien ◽  
M B Millis ◽  
P P Griffin
Keyword(s):  
Early Identification ◽  
Growth Disturbances

L2MXception: An Improved Xception Network for Classification of Peach diseases 

2020 ◽  
Author(s):  
Na Yao ◽  
Fuchuan Ni ◽  
Ziyan Wang ◽  
Jun Luo ◽  
Wing-Kin Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Identification ◽  
Image Data ◽  
Yield Reduction ◽  
Disease Prediction ◽  
Imaging Data ◽  
Regularization Term ◽  
Detection And Identification ◽  
L2 Norm

Abstract Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

scholarly journals Classification of Micro-Calcification in Breast from Mammographic Images using Transfer Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 4835-4841
Keyword(s):  
Transfer Learning ◽  
Early Identification ◽  
Breast Tumor ◽  
Early Detection Of Cancer ◽  
Medical Images ◽  
Term Survival ◽  
Learning Framework ◽  
Mammographic Images

Early detection of cancer is most important for long term survival of patient. Now a days CADx are widely used for early identification of breast cancer automatically. CAD uses significant features to identify and categorize cancer. CADx based on Convolutional Neural Network are becoming popular now a days due to extracting relevant features automatically. CNNs can be trained from scratch for medical images due to various input sizes and tumor structures. But due to limited amount of medical images available for training ,we have used transfer learning approach.We developed a deep learning framework based on CNN to discriminate the breast tumor either benign or malignant using transfer learning. We used digital mammographic images containing both views from CBIS-DDSM database. We have achived training(100%) and validation accuracy greater than 90% with minimum training and validation loss. We have also compared the reaults with transfer learning using pretrained network alexnet and googlenet on same dataset.

Semi-Presidentialism

2020 ◽  
pp. 314-338
Author(s):  
Sophie Boyron
Keyword(s):  
Comparative Politics ◽  
Early Identification ◽  
Constitutional Law ◽  
Regime Type ◽  
Fifth Republic ◽  
Comparative Constitutional Law ◽  
The World ◽  
Presidential Regimes

This chapter discusses semi-presidentialism, a relative newcomer to the disciplines of both comparative constitutional law and comparative politics. It first retraces the early transformation of the regime of the French Fifth Republic from parliamentary to semi-presidential regime. Secondly, the chapter analyses the early identification of this regime type and the difficulties encountered in defining it. The attempts to assess this regime are then examined. Afterwards, the chapter sketches the migration of semi-presidentialism around the world so as to understand its present spread. Finally, it suggests broadening the basis for the classification of semi-presidential regimes by highlighting the key role played by institutions other than the executive and legislature.

scholarly journals L2MXception: an improved Xception network for classification of peach diseases

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Na Yao ◽  
Fuchuan Ni ◽  
Ziyan Wang ◽  
Jun Luo ◽  
Wing-Kin Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Identification ◽  
Image Data ◽  
Yield Reduction ◽  
Disease Prediction ◽  
Imaging Data ◽  
Regularization Term ◽  
Detection And Identification ◽  
L2 Norm

Abstract Background Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue. Results This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions The proposed L2MXception network may have great potential in early identification of peach diseases.

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