Investigation of deep learning approaches for biomedical data classification

Deep learning is a new and promising area in machine learning to solve problems of artificial intelligence. Such new approaches are required in automated medical decision making since non-automated process is more expensive, labor intensive and thus exposed to human-supplied errors. In the literature, common fields that make use of the deep learning approaches are computer vision, natural language processing and speech recognition. Even though the performance of deep architectures in these fields is reported to be impressive, the use of deep learning in the field of biomedicine is scarce. This study aims to investigate whether the deep learning approaches are also capable of producing successful results in the biomedical area, as well. For this aim, mainly deep learning approaches of Deep Belief Networks and Convolutional Neural Networks are employed. The investigation is carried out by conducting several experiments that include comparison with some well-known state-of-art methods which have been proven to be successful in the relevant literature. Additionally, in the scope of the thesis, any possible improvements to the results of basic deep learning methodology are also investigated in order to enhance the classification performance. To this end, some preprocessing steps such as feature selection, removal of the imbalance in data, and texture analysis of medical images are applied in the experiments. To properly assess the performance, various biomedical benchmark datasets including low and high dimensional biomedical data, computed tomography images, microarray based cancer data and skin cancer images have been utilized. The experiments show that deep learning approaches show a promising direction towards supporting medical decision making.