This book explores how advanced machine learning techniques are transforming healthcare, highlighting innovative applications in disease diagnosis, treatment, and healthcare management. It shows that adaptation of machine learning can bring significant benefits for the sustainability of healthcare informatics in the era of 4.0 IR.
This book explores how advanced machine learning techniques are transforming healthcare, highlighting innovative applications in disease diagnosis, treatment, and healthcare management. It shows that adaptation of machine learning can bring significant benefits for the sustainability of healthcare informatics in the era of 4.0 IR.
With contributions from researchers and field experts, the book covers key topics such as predictive analytics, medical image processing, and personalized healthcare. Each chapter provides detailed methodologies, datasets, and experimental results, with practical insights into AI-driven diagnostics, patient monitoring, and decision-support systems.
Designed for those seeking to apply machine learning in healthcare and to advance healthcare informatics, this book is a valuable resource for researchers, professionals, and students.
1. A Robust Deep Learning Based Hybrid Model to Detect Covid-19 Using
Chest X-ray
2. Transfer learning-based approach to detect crop disease using
android application
3. A Proposed Sequential Network Analysis for
Identification of Hub Genes for Therapeutics in Tuberculosis and Its
Overlaying Non-Communicable Disorders
4. Transfer-learning-based Feature
Extractor Performance Analysis to Classify Black Gram Leaf Disease
5. Early
Prediction of Breast Cancer using Deep Learning Models
6. Chest-InfNet: A
Deep Learning Architecture for Lung Diseases Detection and Infected Region
Localization from Chest X-Ray Images
7. Ensemble-Based Transfer Learning
Approach for Brain Tumor Segmentation from MRI Images
8. Preventing Skin
Cancer through Improved Skin Lesion Recognition: An Attention-Triplet and
Multi-Layer Ensemble Based CNN Approach
9. Gastrointestinal Disease
Classification through Explainable and Cost-Sensitive Deep Neural Networks
with Supervised Contrastive Learning
10. COVID-19 Distance Learning
Understanding Classification using Scalogram Based on Transfer Learning and
Principal Feature Classifier from EEG Signals
11. Large Ensemble of
Transfer-Learned Models for Plant Disease Recognition from Diverse Leaf
Images
12. Computer-Aided Strategy to Diagnose Lung Cancer from CTScan Images
Using Inception Architecture
13. Automated Bone Age Assessment using Deep
Learning with Attention Module
14. Towards Bengali Health Text Identification
using Deep Learning Technique
15. Brain Tumor Detection Using Fine-Tuned
ResNet-101 on Magnetic Resonance Images
16. Automated Agricultural Pests
Identification using Convolutional Neural Network-based Transfer Learning
17.
CTFCP: A Cloud-based Deep Transfer Learning Framework for Analyzing Chest
X-Ray Images to Detect Pneumonia
Nazmul Siddique is a researcher at the School of Computing, Engineering, and Intelligent Systems, Ulster University. He has published over 170 research papers and several books on cybernetics and computational intelligence. His editorial roles in top journals highlight his academic influence and contributions.
Mohammad Shamsul Arefin is a professor at the Department of CSE, CUET, and Dean of Electrical and Computer Engineering. He has over 170 publications in journals and conferences on data mining, distributed computing, and machine learning. His leadership has significantly fostered research growth and academic excellence in many aspects.
Mohammad Abu Yousuf is currently the Vice-Chancellor of Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh, and a Professor at the Institute of Information Technology, Jahangirnagar University. He holds a B.Sc. in Computer Science and Engineering from Shahjalal University of Science and Technology, an M.Eng. in Biomedical Engineering from Kyung Hee University, South Korea, and a Ph.D. in Science and Engineering from Saitama University, Japan. With over 125 publications in peer-reviewed journals, conferences, and book chapters, his research spans Medical Image Processing, Human-Robot Interaction, Computer Vision, and Natural Language Processing.
M. Shamim Kaiser is a professor and Chairman at the Institute of Information Technology, Jahangirnagar University. He has authored over 100 research papers on machine learning, cyber security, and cognitive radio networks. His leadership at IIT has driven academic and research excellence in ICT.
