Читать книгу Computational Analysis and Deep Learning for Medical Care - Группа авторов - Страница 14

The concept of Convolutional Neural Network (CNN) was introduced by Fukushima. The principle in CNN is that the visual mechanism of human is hierarchical in structure. CNN has been successfully applied in various image domain such as image classification, object recognition, and scene classification. CNN is defined as a series of convolution layer and pooling layer. In the convolution layer, the image is convolved with a filter, i.e., slide over the image spatially and computing dot products. Pooling layer provides a smaller feature set.

One major cause of low back pain is disc degeneration. Automated detection of lumbar abnormalities from the clinical scan is a burden for radiologist. Researchers focus on the automation task of the segmentation of large set of MRI data due to the huge size of such images. The success of the application of CNN in various field of object detection enables the researchers to apply various models for the detection of Intervertebral Disc (IVD) and, in turn, helps in the diagnosis of diseases.

The details of the structure of the remaining section of the paper are as follows. The next section deals with the study of the various CNN models. Section 1.3, presents applications of CNN for the detection of the IVD. In Section 1.4, comparison with state-of-the-art segmentation approaches for spine T2W images is carried out, and conclusion is in Section 1.5.