Case Study: Deep Convolutional Networks in Healthcare
CitationAvci, M., Sarıgül, M., Ozyildirim, B.M. (2020). Case Study: Deep Convolutional Networks in Healthcare. Studies in Computational Intelligence, 867, 61-89. https://doi.org/10.1007/978-3-030-31764-5_3
Technological improvements lead big data producing, processing and storing systems. These systems must contain extraordinary capabilities to overcome complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis method. It becomes one of the most popular and up-to-date artificial neural network types with deep representation ability. Another powerful ability of deep learning is providing feature learning through convolutional neural networks. Deep learning has wide implementation areas in medical applications from diagnosis to treatment. Various deep learning methods are applied to the biomedical problems. In many applications, deep learning solutions are modified in accordance with the requirements of the problems. Through this chapter the most popular and up-to-date deep learning solutions to biomedical problems are discussed. Studies are analyzed according to problem characteristic, importance of solution, requirements and deep learning approaches to solve them. Since the deep learning systems have very effective image and pattern recognition ability, biomedical imaging becomes one of the most suitable application areas. During the first diagnosis and continuous tracking phase of the patients, deep learning systems offer very effective aids to the medicine. Although organ, disease or data type classifications are possible for biomedical application categorization, organ and disease combination are taken into consideration in the chapter. © Springer Nature Switzerland AG 2020.