Convolutional Neural Networks for Medical Image Processing Applications

This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

-aban Öztürk is an Associate Professor in Amasya University, Amasya, Turkey. He obtained his B.S., M.S. Ph.D. in Electrical and Electronics Engineering from Selçuk University, Turkey in 2011, 2015, and 2019, respectively. He lectures in artificial intelligence and image processing related courses at the Amasya University. Also, he is the head of the Visual Understanding in Biomedical Images laboratory. His research interests encompass artificial intelligence, medical image analysis and deep learning applications. He has more than 50 published articles and proceedings.

Convolutional neural networks for segmentation in short-axis cine cardiac magnetic resonance imaging: review and considerations. Comparison of Traditional Machine Learning Algorithms and Convolution Neural Networks for Detection of Peripheral Malarial Parasites in Blood Smears. Deep Learning-Based Computer-Aided Diagnosis System for Attention Deficit Hyperactivity Disorder Classification Using Synthetic Data. Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation from CT Images. Convolutional Neural Networks for Medical Image Analysis. Ulcer and Red Lesion Detection in Wireless Capsule Endoscopy Images using CNN. Do More with Less: Deep Learning in Medical Imaging. Automatic Classification of fMRI Signals from Behavioral, Cognitive and Affective Tasks Using Deep Learning. Detection of COVID-19 in Lung CT-Scans using Reconstructed Image Features. Dental image analysis: Where deep learning meets dentistry. Malarial Parasite Detection in Blood Smear Microscopic Images: A Review on Deep Learning Approaches. Automatic Classification of Coronary Stenos is using Convolutional Neural Networks and Simulated Annealing. Deep Learning Approach for Detecting COVID-19 from Chest X-ray Images.