Intelligent Systems Design and Applications : Smart Healthcare, Volume 1

• Autore: Abraham Ajith
• Pubblicazione: Cham : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (0 pages)
• Altri autori: BajajAnu HanneThomas SiarryPatrick
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Identifying Lung Cancer from CT-Scan Images with VGG16 Convolutional Neural Net -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Model Building & -- Training -- 3.4 Results & -- Discussion -- 4 Conclusion -- References -- Cause and Effect of Dementia on Women in Technological Environment -- 1 Introduction -- 2 Literature Survey -- 3 Cause of Dementia -- 4 Comparative Analysis of Dementia -- 5 An Intelligent Dementia Detector -- 6 Conclusion -- References -- Machine Learning Techniques for Pancreatic Cancer Detection -- 1 Introduction -- 1.1 Pancreatic Cancer: A Lethal Challenge -- 1.2 Importance of Early Detection -- 1.3 Role of Machine Learning in Cancer Detection -- 2 Literature Review -- 3 Empowering Cancer Detection with ML -- 3.1 Basic Machine Learning Algorithms for Pancreatic Cancer Detection -- 3.2 Advance Machine Learning Techniques -- 3.3 Deep Learning Methods for Pancreatic Cancer Detection -- 4 Materials and Methods -- 4.1 Data Collection -- 4.2 Data Preprocessing -- 4.3 Machine Learning Algorithms -- 4.4 Interpretability and Explainability -- 4.5 Performance Evaluation -- 5 Results -- 5.1 Performance Evaluation Metrics: -- 5.2 Performance Comparison -- 5.3 Feature Importance Analysis: -- 6 Discussion -- 7 Conclusion -- References -- Integrating Artificial Intelligence and Data Analytics for Enhanced Healthcare Management: Innovations and Challenges -- 1 Introduction -- 2 Background -- 3 Research Methodology -- 4 Applications of AI in Healthcare Industry -- 4.1 AI for Drug Discovery -- 4.2 AI for Clinical Trials -- 5 Challenges of Artificial Intelligence in Healthcare Management -- 5.1 Data Availability and Its Quality -- 5.2 Data Privacy and Security -- 5.3 Interoperability -- 6 Conclusion -- References. ; An Intelligent Model for Post Covid Hearing Loss -- 1 Introduction -- 2 Mathematical Model -- 3 Non-negativity and Boundedness of the Solutions -- 4 Stability Analysis -- 5 Numerical Simulations -- 6 Result -- 7 Conclusion and Future Work -- References -- Medical Reports Simplification Using Large Language Models -- 1 Introduction -- 2 Materials and Methods -- 2.1 Fine-Tuning T5 -- 3 Results and Discussion -- 4 Conclusion -- References -- Analysis of Magnetic Resonance Imaging for Parkinson's Disease -- 1 Introduction -- 2 Dataset Description -- 3 Methodology -- 4 Simulation Results -- 5 Conclusion -- References -- Study on Health Issue Identification Using Deep Learning and Convolutional Neural Networks -- 1 Introduction -- 1.1 Benefits of Deep Learning in the Medical Field -- 1.2 Enhanced Diagnostics -- 2 Methods -- 3 Conclusion -- References -- Early-Stage Lung Cancer Prediction: A Machine Learning Approach -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 Exploratory Data Analysis -- 3.4 Machine Learning Algorithms -- 3.5 Model Evaluation -- 3.6 GridSearchCV -- 3.7 Feature Importance -- 4 Results -- 4.1 Results of EDA -- 4.2 Model Evaluation -- 5 Discussion -- 6 Conclusion -- References -- Convolutional Neural Network-Based Brain Tumor Segmentation Using Detectron2 -- 1 Introduction -- 2 Problem Statement -- 3 Convolutional Neural Networks -- 3.1 Mask R-CNN -- 3.2 Detectron2 -- 4 Results and Discussion -- 4.1 Quantitative Evaluation -- 4.2 Qualitative Evaluation -- 5 Conclusion and Future Works -- References -- Deep Learning-Based Histopathological Analysis for Colon Cancer Diagnosis: A Comparative Study of CNN and Transformer Models with Image Preprocessing Techniques -- 1 Introduction -- 2 Related Works -- 3 Proposed System -- 3.1 Preprocessing -- 3.2 Deep Learning Networks. ; 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Data Augmentation -- 4.3 Performance Analysis -- 5 Conclusion -- References -- Detecting Parkinson's Disease at an Early Stage Through Machine Learning Analysis of Brain MRI Images -- 1 Introduction -- 2 Methodology -- 2.1 Data Collection and Pre-processing -- 2.2 Skull Stripping Segmentation -- 2.3 Feature Extraction -- 2.4 Machine Learning Classification -- 2.5 Validation -- 3 Results -- 3.1 Segmentation -- 3.2 Feature Extraction -- 3.3 Brain Shape Analysis -- 3.4 Performance of Machine Learning Classifiers -- 4 Discussion -- 5 Conclusion -- References -- Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Preprocessing Module -- 3.2 Classification Module -- 3.3 Proposed Feature Integration -- 4 Results -- 4.1 Experimental Environment -- 4.2 Dataset -- 4.3 Evaluation Criteria -- 4.4 Experimental Results -- 5 Conclusion -- References -- Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Preparation -- 2.2 Machine Learning Model -- 2.3 Dataset -- 3 Result and Discussion -- 4 Conclusion -- References -- Classification of Arrhythmia Using Deep Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Objectives -- 1.3 Advantages of the Proposed Methodology -- 2 Background -- 2.1 Limitations of the Existing Model -- 3 Proposed Methodology -- 3.1 Dataset Pre-processing: -- 3.2 Terms -- 4 Results -- 5 Future Work -- 6 Conclusion -- References -- An Integrated Machine Learning and IoT Based Approach for Enhanced Healthcare Efficiency and Personalized Treatment -- 1 Introduction -- 2 Methodology -- 2.1 Sensors Used in This Research -- 2.2 Machine Learning Algorithm. ; 3 Working of the Proposed System -- 4 Result and Discussion -- 5 Conclusion -- References -- CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer -- 1 Introduction -- 2 Related Works -- 2.1 Colposcope Image Datasets -- 2.2 Automated Diagnostic Models on Colposcope Images -- 3 CeLaTis Dataset -- 3.1 Data Acquisition -- 3.2 Acquisition Procedure -- 3.3 Features -- 4 Methodology -- 4.1 Segmentation -- 4.2 Lesion Recognition Network -- 4.3 Classification -- 5 Conclusion -- References -- A Deep Learning Approach With Sparse Autoencoder for Alzheimers Disease Classification -- 1 Introduction -- 2 Research Background -- 3 Dataset -- 4 Proposed Methodology -- 4.1 Preprocessing -- 4.2 Feature Extraction and Dimensionality Reduction -- 4.3 Deep Neural Network -- 5 Results and Discussion -- 6 Conclusion -- References -- An Improved Gradient Based Joint Histogram Equalization Technique for Mammogram Image Contrast Enhancement -- 1 Introduction -- 2 Suggested Methodology -- 2.1 Extraction of the Gradient Image -- 2.2 Contrast Enhancement Using Joint Histogram -- 3 Results and Discussion -- 4 Conclusion -- References -- Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection -- 1 Introduction -- 2 Related Literature -- 3 Methods -- 3.1 DenseNet -- 3.2 AlexNet -- 3.3 ResNet -- 3.4 Vgg 16 -- 4 Results -- 4.1 Experimental Environment -- 4.2 Dataset -- 4.3 Evaluation Criteria -- 4.4 Experimental Results -- 5 Conclusion -- References -- Comparative Analysis for Feature Selection Approaches for Parkinson's Disease Prediction -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Data -- 3.2 Exploratory Analysis of Data -- 3.3 Feature Selection Methods -- 3.4 Classification Algorithm -- 4 Result Analysis -- 4.1 Metrics -- 4.2 Statistical Analysis -- 5 Conclusion and Future Works. ; References -- Autism Spectrum Disorder Prediction: A Machine Learning Approach -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 Exploratory Data Analysis -- 3.4 Machine Learning Algorithms -- 3.5 Model Evaluation -- 3.6 K-fold Cross-Validation -- 3.7 Feature Importance -- 4 Results -- 4.1 Results of EDA -- 4.2 Model Evaluation -- 5 Discussion -- 6 Conclusion -- References -- Epileptic Seizure Detection on EEG Images Using the Decimal Descriptor Pattern -- 1 Introduction -- 2 Methodology -- 2.1 Database -- 2.2 Feature Extraction -- 2.3 Support Vector Machine (SVM) Classifier -- 2.4 Proposed Approach -- 3 Results and Discussion -- 4 Conclusion -- References -- Influence of Rician Noise on Cardiac MR Image Segmentation Using Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 MRI Dataset -- 3.2 Rician Noise in MRI Image -- 3.3 AI Segmentation Models -- 3.4 Assessing the Noise Resilience of the Model -- 4 Result -- 5 Discussion -- 6 Conclusion -- References -- A Single-Stage Deep Learning Approach for Multiple Treatment and Diagnosis in Panoramic X-ray -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset and Annotation -- 2.2 Proposed Method -- 2.3 Experimental Setup -- 3 Results and Discussion -- 4 Conclusion -- References -- Precision Care in Addiction Treatment: A Bayesian-Based Machine Learning Analysis for Adults with Substance Use Disorders -- 1 Introduction -- 2 Related Works -- 2.1 Machine Learning for HealthCare -- 2.2 Machine Learning for SUD Treatment -- 3 Methods -- 3.1 Proposed Structure -- 3.2 Data Pre-Processing and Filtration Criteria -- 4 Results and Discussion -- 4.1 Outcome Obtained Through Cross-Validation Methods -- 5 Conclusion and Future Work -- References. ; Hybrid Network Model for the Prediction of Retinopathy of Prematurity from Neonatal fundus images.
• Titolo autorizzato: Intelligent Systems Design and Applications
• ISBN: 9783031648137
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878050103321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems Series