1st Edition

Convolutional Neural Networks for Medical Image Processing Applications

Edited By Saban Ozturk Copyright 2023
    274 Pages 95 B/W Illustrations
    by CRC Press

    The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits.

    While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. 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.

    Chapter 1: Ulcer and Red Lesion Detection in Wireless Capsule Endoscopy Images using CNN

    Said Charfi, Mohamed El Ansari, Ayoub Ellahyani, and Ilyas El Jaafari

    Chapter 2: Comparison of Traditional Machine Learning Algorithms and Convolution Neural Networks for Detection of Peripheral Malarial Parasites in Blood Smears

    Aravinda C.V., Meng Lin, Udaya Kumar Reddy K R, H N Prakash, Amar Prabhu G, and Sudeepa K.B.

    Chapter 3: Deep Learning-Based Computer-Aided Diagnosis System for Attention Deficit Hyperactivity Disorder Classification Using Synthetic Data

    Gulay Cicek, and Aydın AKAN

    Chapter 4: Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation from CT Images

    A. Emre Kavur, Ludmila I. Kuncheva, and M. Alper Selver

    Chapter 5: Convolutional Neural Networks for Medical Image Analysis

    Rajesh Gogineni, and Ashvini Chaturvedi

    Chapter 6: Convolutional neural networks for segmentation in short-axis cine cardiac magnetic resonance imaging: review and considerations

    Manuel Pérez-Pelegrí, José V. Monmeneu, María P. López-Lereu, and David Moratal

    Chapter 7: Do More with Less: Deep Learning in Medical Imaging

    Shivani Rohilla, Mahipal Jadeja, and Emmanuel S. Pilli

    Chapter 8: Automatic Classification of fMRI Signals from Behavioral, Cognitive and Affective Tasks Using Deep Learning

    Cemre Candemir, Osman Tayfun Bişkin, Mustafa Alper Selver, and Ali Saffet Gönül

    Chapter 9: Detection of COVID-19 in Lung CT-Scans using Reconstructed Image Features

    Ankita Sharma, and Preety Singh

    Chapter 10: Dental image analysis: Where deep learning meets dentistry

    Bernardo Silva, Laís Pinheiro, Katia Andrade, Patrícia Cury, and Luciano Oliveira

    Chapter 11: Malarial Parasite Detection in Blood Smear Microscopic Images: A Review on Deep Learning Approaches

    Kinde Anlay Fante and Fetulhak Abdurahman

    Chapter 12: Automatic Classification of Coronary Stenos is using Convolutional Neural Networks and Simulated Annealing

    Luis Diego Rendon-Aguilar, Ivan Cruz-Aceves, Arturo Alfonso Fernandez-Jaramillo, Ernesto Moya-Albor, Jorge Brieva, and Hiram Ponce

    Chapter 13: Deep Learning Approach for Detecting COVID-19 from Chest X-ray Images

    Murali Krishna Puttagunta, Dr S. Ravi, and Dr C. Nelson Kennedy Babu


    Şaban 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.