Full Description
Over the last decades, there has been a revolution in the use of new intelligent technologies to analyze and interpret medical images for diseases diagnosis, assessment ad treatment. This new volume explores the latest cutting-edge research in medical image analysis. The advanced intelligent technologies discussed include machine learning, ensemble methods in machine learning, deep learning methods and firebase technology, infrared thermography, deep convolution neural networks, and more. Some of the specific uses of these technologies include for brain tumor MRIs, for breast cancer screening, for polycystic ovary syndrome classification, for detecting and monitoring Alzheimer's disease, for monitoring of newborns, for retinal disease diagnosis, for Covid-19 detection, and more.
Contents
1. A Comparative Study of Anisotropic Diffusion Filters for Medical Image Denoising 2. Salt and Pepper Noise Removal Techniques for Medical Image Reconstruction 3. Comparative Analysis of PSP- and WOA-Based Segmentation of Brain Tumor MRIs 4. Breast Cancer Screening Using Fractal Dimension of Chromatin in Interphase Nuclei of Buccal Epithelium 5. Polycystic Ovary Syndrome Classification Based on Machine Learning 6. A Comprehensive Review on Diagnosis of Alzheimer's Disease Using Ensemble Methods and Machine Learning 7. A New Strategy for Prediction of Diabetic Retinopathy Using Deep Learning Methods and Firebase Technology 8. Contactless Monitoring in Newborns Using Infrared Thermography: A Review 9. Retinal Disease Diagnosis Using Machine Learning Techniques 10. Automated Segregation of Lymphoid and Myeloid Blasts in Acute Leukemia Cases Using a Deep Convolutional Neural Network 11. Evaluation of Deep Learning Network Architectures for Medicine Expenditure Prediction in the Healthcare Domain 12. Covid-19 Detection from Chest X-Ray Using a Customized Artificial Neural Network 13. An Automated Deep Learning Approach to Classify ECG signals using AlexNet 14. MLO and CC View of Feature Fusion and Mammogram Classification Using a Deep Convolution Neural Network
