Brain tumor segmentation by auxiliary classifier generative adversarial network

Recently, great progress has been achieved in the building of automatic segmentation and classification systems for use in medical applications utilizing machine learning techniques. These systems have been used to analyze medical images. However, the performance of some of these systems typically decreases when utilizing fresh data. This may be because different data were used for training, which may be because of changes in protocols or imaging equipment, or it may be because of a combination of these factors. In the field of medical imaging, one of the most difficult and important goals is to produce an image that is really medical yet is otherwise wholly distinct from the original images. The fake images that are produced as a consequence boost diagnostic accuracy and make it possible to identify more data, both with the help of computers and in the training of medical professionals. These issues are mostly brought on by low-contrast MR images, particularly in the anatomical regions of the brain, as well as shifts in sequence. Within the scope of this study, we investigate the possibility of producing multiple-sequence MR images by the application of auxiliary classifier-generating adversarial networks (ACGANs). In addition to that, a brand new approach to in-depth learning for tumor segmentation in MR images is provided. In the beginning, a deep neural network is trained to function as a discriminator in GAN data sets consisting of magnetic resonance (MR) images in order to extract the features and also learn the structure of the MR images in their annular layers. After that, the layers that are already fully connected are removed, and the entire deep network is instructed in segmentation for the purpose of diagnosing tumors. The proposed AC-GAN method provides an overall accuracy of 94% on the BraTs2019 database using Adam optimization with a batch size of 30.