Automatic Medical Images Segmentation Based on Deep Learning Networks

Abstract

In recent years, radiography systems have become more used in medical fields, where they are used for diagnosing many diseases. The size of the radiographs differs, as well as the size of the body parts for each patient. So many researchers crop the radiographs manually to facilitate the diagnosis and make it more reliable. Currently, the trend toward deep learning was commended where the deep learning proved its effectiveness in many fields, especially in the medical field, in which it achieves good results in diagnosing the most types of diseases. Deep learning performance increases significantly when the training process is focused on the region of interest. In this paper, segmentation is implemented by used deep learning model on the thoracic region of the radiograph in order to be cropped later. The proposed model provided automatic cropping of the radiographs where a semantic segmentation network is provided by Vgg19 model. A comparison is done with semantic segmentation network provided by Vgg16. The segmentation based on Vgg19 model outperforms Vgg16 model in cropping Chest x-ray images dataset automatically and quickly.

Chest Radiographs Images Retrieval Using Deep Learning Networks

“…Then, a ranked list of the most top 5 similar images is returned that may help a radiologist or specialist doctor to make the right decision. Since the similarity measurement is another issue in CBIR [31], [32] our experiments tested City-block (DF1) and cosine (DF2) distance functions as ( 6) and (7).…”

“…This paper has numerous contributions to the literature. Different DCNNs (Resnet-50, AlexNet, and GoogleNet) models are investigated using our setting and a recent segmentation method [7] in the preprocessing stage. These models are used in the proposed method to classify chest images and exploit them to learn the features of the images.…”