Deep Learning Based Egg Fertility Detection.

This study investigates the implementation of deep learning (DL) approaches to the fertile egg-recognition problem, based on incubator images. In this study, we aimed to classify chicken eggs according to both segmentation and fertility status with a Mask R-CNN-based approach. In this manner, images can be handled by a single DL model to successfully perform detection, classification and segmentation of fertile and infertile eggs.

Automatic phase reversal detection in routine EEG.

Electroencephalograph (EEG), a valuable tool in the clinical evaluation, is readily available, safe and provides information about brain function. EEG interpretation is important for the diagnosis of neurological disorders. The long-term EEG data may be required to document and study neurosciences that include many epileptic activities and phase reversal (PR) etc.

Measuring the Effect of Filters on Segmentation of Developmental Dysplasia of the Hip.

Background: Developmental dysplasia of the hip (DDH) can be detected with ultrasonography (USG) images. However, the accuracy of this method is dependent on the skill of the radiologist. Radiologists measure the hip joint angles without computer-based diagnostic systems.