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This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal Machine Learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using Federated Learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
Machine Learning (ML) and Deep Learning (DL) have become indispensable for medical image analysis due to their capacity to extract meaningful information from large and complex medical image datasets. Currently, medical image datasets are increasing rapidly in size and complexity. Additionally, these algorithms are capable of processing and analyzing enormous amounts of data much more quickly and precisely than manual methods. However, it is challenging to determine which of these two approaches yields more accurate results. To analyze the efficacy of the ML strategy, seven distinct Machine Learning algorithms, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), RandomForest, and AdaBoost, are employed, followed by the best classifier being chosen using GridSearchCV. On the other hand, Convolutional Neural Network (CNN) dependent DL frameworks are designed for running an analysis of the Deep Learning approach.
Domain Adaptation in Medical Imaging: Evaluating the Effectiveness of Transfer Learning
Advancing Brain Tumour Detection: Transfer Learning-Based Approach Fused with Squeeze-and-Excitation (SE) Attention Mechanism in Computer Vision
A Precise Cervical Cancer Classification in the Early Stage Using Transfer Learning-Based Ensemble Method: A Deep Learning Approach
Unveiling Diagnostic Precision: Evaluating Machine Learning and Deep Learning Approaches for Pneumonia Recognition of COVID-19 Patients Using Chest X-Rays
Advanced Hybrid Deep Learning Model for Precise Multiclass Classification of Bone Marrow Cancer Cells
Privacy-Preserving Vision-Based Detection of Pox Diseases Using Federated Learning
Unveiling the Unique Dermatological Signatures of Human Pox Diseases Through Deep Transfer Learning Model Based on DenseNet and Validation with Explainable AI
Improved Classification of Kidney Lesions in CT Scans Using CNN with Attention Layers: Achieving High Accuracy and Performance
Enhancing Breast Cancer Detection Systems: Augmenting Mammogram Images Using Generative Adversarial Networks
Connections into a Multi-channel CNN for Lung Cancer Detection in Digital Pathology
Advancing Breast Cancer Diagnosis: Attention-Enhanced U-Net for Breast Cancer Segmentation
Privacy Preserving Breast Cancer Prediction with Mammography Images Using Federated Learning