Abstract The present invention is a system and method for a spatio-temporal neural The present invention is a system and method for a spatio-temporal neural network for non-modular high content pathological screening. The method includes following steps: obtaining images from various sources, annotating the images by medical expert through user interface, extracting features from the annotated images by a feature extractor, classifying lumen area on each of the annotated images, re-authenticating the classified lumen through user interface, exporting images in requisite format and training online and offline by user without user intervention. The annotating of images is manual The method also includes steps of data collection and domain expert support, de-noising of the noise that arises from straining process, morphological feature extraction and template based learning for classification and synthesis of images. The system for a spatio-temporal neural network for non-modular high content pathological screening includes, a high content screening (HCS) device, an annotating device, a feature extractor and a user interface. The HCS device provides data required for screening. The annotating device is used for annotation of the data by the medical expert practitioners by using a user interface to formulate ground truth and the feature extractor is used for providing stage I optics. The user interface is a display with server.
