Читать книгу Computational Intelligence and Healthcare Informatics - Группа авторов - Страница 47

Considering scarcity of radiologists in less economically developed countries, deep learning models are used for medical imaging for detecting abnormalities in CXR images. There are 14 pathologies whose severity leads to mortality; therefore, many researchers try to detect all 14 pathologies. Generally, DL models are classified into two categories, namely, ensemble and non-ensemble models. Many researchers deployed parameters initialized from ImageNet dataset and then fine tune their proposed network as per the task. In order to deal with different issue, different pre-processing techniques are employed by the authors. The ChestX-ray14 dataset is the popular dataset which is experimented mostly as it contains large number of images with annotation. Cardiomegaly is the major chest pathology detected by many authors due to its spatially spread nature. We have also discussed factors affecting performance of models along with their significance. Finally, we have compared existing models on the basis of different parameters so that it will be easy to carry out future research to develop more robust and accurate model for the thoracic image analysis using deep models.