Experimental Results and Discussion
7.5 Conclusion
References
Notes
15 8 Neural Network Optimizers for Brain Tumor Image Detection 8.1 Introduction 8.2 Related Works 8.3 Background 8.4 Case Study ‐ Brain Tumor Detection 8.5 Conclusion References Note
16 9 Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry of Human Head Scans 9.1 Introduction 9.2 Literature Review 9.3 Methodology 9.4 Materials and Metrics 9.5 Results and Discussion 9.6 Conclusion References Note
17 10 Conclusion References Note
18 Index
List of Tables
1 Chapter 2Table 2.1 Training setup for AlexNet.Table 2.2 Performance evaluation of classification accuracy.Table 2.3 Comparative analysis of existing methods and the proposed method.Table 2.4 Comparative analysis of the existing methods and the proposed meth...
2 Chapter 3Table 3.1 Example behavior of an RV monitor for property
3 Chapter 4Table 4.1 The performance (test‐accuracy) of the proposed capsule and basic ...
4 Chapter 5Table 5.1 Methods for cell clustering and visualization.Table 5.2 Methods for the bifurcation/branch identification ordering of cell...Table 5.3 Gene‐level analysis tools (identifying differentially expressed ge...Table 5.4 Unsupervised data integration methods.Table 5.5 Supervised data integration tools.Table 5.6 Semi‐supervised data integration tools.Table 5.7 List of AI‐based computational tools for drug discovery.
5 Chapter 6Table 6.1 Distribution of breast cancer patients based on race or ethnicity.Table 6.2 Training data obtained by applying SMOTE and ENN.Table 6.3 Default parameter values for corresponding base learners.Table 6.4 Discovered rules using the class association rule method (conseque...Table 6.5 Extracted rules using the class association rule technique (conseq...Table 6.6 Rules generated using the class association rule technique (conseq...Table 6.7 Performance (accuracy, precision, recall/sensitivity, specificity)...Table 6.8 Performance (AUC, F1, and G‐mean) of the SL and three ML algorithm...
6 Chapter 7Table 7.1 Classification accuracy (%) obtained by the proposed method and it...Table 7.2 Performance analysis of different local descriptors and proposed m...Table 7.3 Performance analysis of the proposed method and several deep archi...
7 Chapter 8Table 8.1 Accuracy of artificial neural network models for differing optimiz...Table 8.2 Performance analysis of neural network optimizers using early stop...Table 8.3 Comparison of the proposed method with state‐of‐the‐art methods.
8 Chapter 9Table 9.1 Histogram‐based image features.Table 9.2 GLCM‐based image features.Table 9.3 GLRLM‐based image features.Table 9.4 Correlation of the selected features with the class label.Table 9.5 Test phase predictions for the selected volumes from the IBSR‐18 a...
List of Illustrations
1 Chapter 2Figure 2.1 AlexNet architecture.Figure 2.2 Metastasis‐PET image.Figure 2.3 Lymph node‐fused PET‐CT image.Figure 2.4 Classification results of the primary tumor (T).Figure 2.5 Classification accuracy of the primary tumor (T).Figure 2.6 Loss function for the primary tumor (T).Figure 2.7 Confusion matrix for the primary tumor (T).Figure 2.8 Classification results of the metastasis (M).Figure 2.9 Classification accuracy of metastasis (M).Figure 2.10 Loss function for the metastasis (M).Figure 2.11 Confusion matrix for the metastasis (M).Figure 2.12 Classification results of the lymph node (N).Figure 2.13 Classification accuracy of the lymph node (N).Figure 2.14 Loss function for the lymph node (N).Figure 2.15 Confusion matrix for the lymph node (N).
2 Chapter 3Figure 3.1 The heart‐pacemaker system shows leads connected to the right atr...Figure 3.2 Timing diagram for a DDD mode pacemaker (adapted from Pinisetty e...Figure 3.3 Timing information of electrocardiogram signals.Figure 3.4 Model checking.Figure 3.5 Conformance testing with formal methods.Figure 3.6 Verification monitor.Figure 3.7 Enforcement mechanism.Figure 3.8 Overview of the RV monitoring approach (from Pinisetty et al. (20...Figure 3.9 Timed automaton defining property
