Computational Analysis and Deep Learning for Medical Care. Группа авторов

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      4 Chapter 4Table 4.1 Retrieval performance of metric learning for VGG19.Table 4.2 Performance of retrieval techniques of the trained VGG19 among fine-tu...Table 4.3 PR values of various models—a comparison for CT image retrieval.Table 4.4 Recall vs. precision for proposed content-based image retrieval.Table 4.5 Loss function of proposed deep regression networks for training datase...Table 4.6 Loss function of proposed deep regression networks for validation data...Table 4.7 Land mark details (identification rates vs. distance error) for the pr...Table 4.8 Accuracy value of the proposed system.Table 4.9 Accuracy of the retrieval methods compared with the metric learning–ba...

      5 Chapter 6Table 6.1 Definition of the abbreviations.

      6 Chapter 7Table 7.1 Performance of proposed models on English dataset.Table 7.2 Performance of proposed model on Bangla dataset.Table 7.3 Performance of proposed model on Math Symbol dataset.

      7 Chapter 8Table 8.1 ABCD factor for TDS value.Table 8.2 Classify mole according to TDS value.

      8 Chapter 9Table 9.1 The confusion matrix for different classifier.Table 9.2 Performance analysis of different classifiers: Random Forest, SVM, Naï...

      9 Chapter 10Table 10.1 Result analysis.

      10 Chapter 11Table 11.1 Comparison of different techniques and tumor.

      11 Chapter 13Table 13.1 Cognitive functions related with routine activities.Table 13.2 Situation and design features.Table 13.3 Accuracy of prediction.

      12 Chapter 14Table 14.1 Accuracy comparison and mean of algorithms with baseline records.Table 14.2 Accuracy comparison and mean of algorithms with current records.

      13 Chapter 15Table 15.1 Variances of Convolutional Neural Network (CNN).Table 15.2 Various issues challenges faced by researchers for using deep learnin...

      14 Chapter 17Table 17.1 Comparative analysis: classification accuracy for 10 datasets—analysi...

      15 Chapter 18Table 18.1 Comparison among data mining, machine learning, and deep learning.

      Guide

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