Automated detection of colorectal tumors based on artificial intelligence

Background This study developed a diagnostic tool to automatically detect normal, unclear and tumor images from colonoscopy videos using artificial intelligence. Methods For the creation of training and validation sets, 47,555 images in the jpg format were extracted from colonoscopy videos for 24 patients in Korea University Anam Hospital. A gastroenterologist with the clinical experience of 15 years divided the 47,555 images into three classes of Normal (25,895), Unclear (2038) and Tumor (19,622). A single shot detector, a deep learning framework designed for object detection, was trained using the 47,255 images and validated with two sets of 300 images—each validation set included 150 images (50 normal, 50 unclear and 50 tumor cases). Half of the 47,255 images were used for building the model and the other half were used for testing the model. The learning rate of the model was 0.0001 during 250 epochs (training cycles). Results The average accuracy, precision, recall, and F1 score over the category were 0.9067, 0.9744, 0.9067 and 0.9393, respectively. These performance measures had no change with respect to the intersection-over-union threshold (0.45, 0.50, and 0.55). This finding suggests the stability of the model. Conclusion Automated detection of normal, unclear and tumor images from colonoscopy videos is possible by using a deep learning framework. This is expected to provide an invaluable decision supporting system for clinical experts.

Decision Supporting System For Malang City Baseball and Softball Athletes Selection Using The Vikor Method

Decision support system is a computer-based system that is used to solve problems by semi-structured and unstructured conditions. In this case, the best solution can be found from certain criteria and provided alternatives and is easy to use by users. In this study, researchers designed and built a website-based decision support system to select athletes in PERBASASI Malang with the provided test criteria, namely the hit test, catch test, throw test, and run test. The purpose of this study was to provide recommendations for participants who passed the selection based on test scores that have been processed using the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. From the results of calculations, the use of the VIKOR method for this case study produced an accuracy value of 90.90%, a precision value of 93.33%, and a recall value of 93.33%. In addition, the VIKOR sensitivity test showed a consistent ranking of the calculation of the value with veto (value of v less than 0.5), by consensus (value of v is 0.5), and voting by majority rule (value of v more than 0.5). User testing that was applied by using each level of the existing account, i.e. administrator account level, selection account level, and members account level conducted on this information system concluded that the system that was built was running smoothly and was easy to use.