Читать книгу Computational Analysis and Deep Learning for Medical Care - Группа авторов - Страница 13

Leena Silvoster M.1* and R. Mathusoothana S. Kumar2

¹ Department of Computer Science Engg, College of Engg, Attingal, Thiruvananthapuram, Kerala, India

² Department of Information Technology, Noorul Islam University, Tamilnadu, India

Abstract

The widespread publicity of Convolutional Neural Network (CNN) in various domains such as image classification, object recognition, and scene classification has revolutionized the research in machine learning, especially in medical images. Magnetic Resonance Images (MRIs) are suffering from severe noise, weak edges, low contrast, and intensity inhomogeneity. Recent advances in deep learning with fewer connections and parameters made their training easier. This chapter presents an in-depth review of the various deep architectures as well as its application for segmenting the Intervertebral disc (IVD) from the 3D spine image and its evaluation. The first section deals with the study of various traditional architectures of deep CNN such as LeNet, AlexNet, ZFNet, GoogleNet, VGGNet, ResNet, Inception model, ResNeXt, SENet, MobileNet V1/V2, and DenseNet. It also deals with the study of the parameters and components associated with the models in detail. The second section discusses the application of these models to segment IVD from the spine image. Finally, theoretically performance and experimental results of the state-of-art of the literature shows that 2.5D multi-scale FCN performs the best with the Dice Similarity Index (DSC) of 90.64%.

Keywords: CNN, deep learning, intervertebral disc degeneration, MRI segmentation