Sensitivity analysis in core diagnostics

Herb, Joachim; Périn, Y.; Yum, S.; Mylonakis, Antonios; Demazière, Christophe; Vinai, Paolo; Yu, Miao; Wingate, James; Hursin, Mathieu

doi:10.1016/j.anucene.2022.109350

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It examines the significance of each source variable’s characteristic for every outcome. Comparing the system outcomes with all factors involved and the model with one element eliminated, or maintaining the quantities of all other factors and simply adjusting the strength of one input element, allows for the measurement of the effect of each input variable [ 375 ]. Sensitivity and Specificity analysis is essential for the actual uses of deep learning applications in the real life; it clearly and unambiguously demonstrates the extent to which CNN model output depends on each factor and gives medical practitioners and specialists more authority, particularly when the recent discoveries are catastrophic events that may represent additional anomalies that surpasses the predictive power of CNN models [ 376 , 377 ].…”

Section: Workflowmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

show abstract

Section: Workflowmentioning

confidence: 99%