The book discusses the impact of machine learning and computational intelligent algorithms on medical image data processing, and introduces the latest trends in machine learning technologies and computational intelligence for intelligent medical image analysis. The topics covered include automated region of interest detection of magnetic resonance images based on center of gravity; brain tumor detection through low-level features detection; automatic MRI image segmentation for brain tumor detection using the multi-level sigmoid activation function; and computer-aided detection of mammographic lesions using convolutional neural networks.

Covers a broad range of potential machine learning and computational intelligence paradigms for medical image analysis Includes an in-depth analysis of hybrid machine intelligence supported by real-world examples Supplemented by coding examples and video demonstrations for each chapter

Autorentext

Siddhartha Bhattacharyya completed his Ph.D. in Computer Science and Engineering at Jadavpur University, India, in 2008. Currently he is the Principal of RCC Institute of Information Technology, Kolkata, India. In addition, he is a Professor of Computer Application and Dean (Research and Development) of the institute. He served as the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence (IJACI), published by IGI Global. He is the Associate Editor of the International Journal of Pattern Recognition Research, IEEE Access, Evolutionary Intelligence and a member of Applied Soft Computing editorial board. His research interests include soft computing, pattern recognition, hybrid intelligence and quantum computing, and he has published over 230 research articles and patents.

Debanjan Konar is an Assistant Professor at the Department of Computer Science and Engineering at Sikkim Manipal Institute of Technology, India. He is pursuing his Ph.D. at the Indian Institute of Technology, Delhi in Quantum Inspired Soft Computing. His research interests include quantum inspired soft computing, deep learning, machine learning, and natural language processing. He has published several papers in these areas in leading journals and IEEE international conferences. He is also a reviewer for various international journals and conferences.

Chinmoy Kar is an Assistant Professor at the Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, and is pursuing his Ph.D. in Image Recognition at the Maulana Abul Kalam Azad University of Technology. His research interests include image processing and computational intelligence, and he actively publishes in these areas.

Kalpana Sharma is a Professor and Head of the Department of Computer Science and Engineering at SMI. She completed her Ph.D. Wireless Sensor Network Security at Manipal University in 2011. Her research interests include wireless sensor networks, security and real time systems.

Jan Platos received his Master's degree in Computer Science from the VSB-Technical University of Ostrava in the Czech Republic in 2006 and his Ph.D. in Applied Mathematics from the same university in 2010. Currently, Jan is an Assistant Professor at the Department of Computer Science, Faculty of Electrical Engineering and Computer Science at the VSB-Technical University of Ostrava. Jan is interested in various areas of computer science, including data compression and bio-inspired algorithms, information retrieval, data mining, data structures and data prediction. Jan is the co-author of more than 160 scientific papers published in proceedings and journals.

Inhalt
Preface.- Introduction.- Brain Tumor Segmentation from T1 Weighted MRI Images Using Rough Set Reduct and Quantum Inspired Particle Swarm Optimization.- Automated Region of Interest detection of Magnetic Resonance (MR) images by Center of Gravity (CoG).- Brain tumors detection through low level features detection and rotation estimation.- Automatic MRI Image Segmentation for Brain tumors detection using Multilevel Sigmoid Activation (MUSIG) function.- Automatic Segmentation of pulmonary nodules in CT Images for Lung Cancer detection using self-supervised Neural Network Architecture.- A Hierarchical Fused Fuzzy Deep Neural Network for MRI Image Segmentation and Brain Tumor Classification.- Computer Aided Detection of Mammographic Lesions using Convolutional Neural Network (CNN).- Conclusion.