Machine Learning for Healthcare Applications. Группа авторов

References 20 Automated Diagnosis of COVID-19 Using Reinforced Lung Segmentation and Classification Model 20.1 Introduction 20.2 Diagnosis of COVID-19 20.3 Genetic Algorithm (GA) 20.4 Related Works 20.5 Challenges in GA 20.6 Challenges in Lung CT Segmentation 20.7 Proposed Diagnosis Framework 20.8 Result Discussion 20.9 Conclusion References

      9  Part 5: CASE STUDIES OF APPLICATION AREAS OF MACHINE LEARNING IN HEALTHCARE SYSTEM 21 Future of Telemedicine with ML: Building a Telemedicine Framework for Lung Sound Detection 21.1 Introduction 21.2 Related Work 21.3 Strategic Model for Telemedicine 21.4 Framework for Lung Sound Detection in Telemedicine 21.5 Experimental Analysis 21.6 Conclusion References 22 A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images 22.1 Introduction 22.2 Literature Review 22.3 Proposed Work 22.4 Experimental Results and Discussion 22.5 Conclusion References 23 Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer 23.1 Introduction 23.2 Clinically Correlated Texture Features 23.3 Machine Learning Techniques 23.4 Result Analysis and Discussions 23.5 Conclusions References 24 Analysis of Machine Learning Technologies for the Detection of Diabetic Retinopathy 24.1 Introduction 24.2 Related Work 24.3 Dataset Used 24.4 Methodology Used 24.5 Analysis of Results and Discussion 24.6 Conclusion References

      10  Index

      11  End User License Agreement

      List of Tables

      1 Chapter 2Table 2.1 Sample Dataset for Phase-I.Table 2.2 Accuracy of the modelTable 2.3 Precision of the model.Table 2.4 Recall of the model.Table 2.5 F1-score of the model.

      2 Chapter 4Table 4.1 Sample rule set for the proposed expert system.

      3 Chapter 5Table 5.1 Description of network architecture.Table 5.2 Description of the hyper parameters.

      4 Chapter 6Table 6.1 Comparison of existing software apps.Table 6.2 Comparison of previous approaches.Table 6.3 Comparison of various DCNNs.

      5 Chapter 7Table 7.1 Examples of some types of ontologies.

      6 Chapter 8Table 8.1 Age & Gender of Subjects.Table 8.2 Percentage of Correct Answers in AB task and α of subjects according t...

      7 Chapter 9Table 9.1 Electrical properties of human tissue.Table 9.2 Dataset creation.Table 9.3 Tabular representation of Classification Reports using KNN, Decision T...

      8 Chapter 10Table 10.1 Performance of SA on drug reviews using ML models.

      9 Chapter 11Table 11.1 Classification report of our machine learning model.Table 11.2 Summary of hyper parameter tuning.

      10 Chapter 12Table 12.1 Classification Report.

      11 Chapter 13Table 13.1 Causes and symptoms for pneumothorax, pneumonia, pleural effusion and...Table 13.2 Causes and symptoms for nodule, mass, cardiomegaly, edema and consoli...Table 13.3 Causes and symptoms for pleural thickening, infiltration, fibrosis an...Table 13.4 Comparison of true label and predicted label for various diseases.

      12 Chapter 14Table 14.1 Difference between acute stage and chronic stages of leukemia.

      13 Chapter 16Table 16.1 Patient’s condition for decision making.

      14 Chapter 17Table 17.1 Sample covid-19 patient details with different age group.

      15 Chapter 18Table 18.1 Related work table.

      16 Chapter 19Table 19.1 COVID-19 Dataset Sample.Table 19.2 Sample of risk wise performance comparison of actual vs predicted inf...Table 19.3 Sample of Rule Base Generation from Decision Tree.Table 19.4 Classification of Countries based on Decision Tree Rule Generation.Table 19.5 Cluster Groups of k-means Clustering Algorithm.Table 19.6 Classification accuracy of proposed algorithms.Table 19.7 Sample of classification of countries based on output variables.Table 19.8 Risk measurement of output variables.Table 19.9 Sample of classification of countries based on risk measurement.

      17 Chapter 20Table 20.1 Values of k_m and k_v.

      18 Chapter 21Table 21.1 Performance measures of different wavelet by Modified-Random Forest c...Table 21.2 Accuracy comparison with db4 feature extraction using modified RF alg...Table 21.3 Comparison