VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images.

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity.

Deep learning supported disease detection with multi-modality image fusion.

Multi-modal image fusion techniques aid the medical experts in better disease diagnosis by providing adequate complementary information from multi-modal medical images. These techniques enhance the effectiveness of medical disorder analysis and classification of results. This study aims at proposing a novel technique using deep learning for the fusion of multi-modal medical images.

Implementation of Software Agents and Advanced AoA for Disease Data Analysis.

To eliminate the possibilities of getting various contradicting solutions to a single problem during diagnosis, a single regular Agent oriented Approach (AoA) is replaced by Intelligent Artificial Agents that act like human and even dynamically decide in any situations known as Intelligent Searching Approach (ISA) is proposed. These agents are used to analyse the medical forums and results or findings are derived accurately than any manual approach. Multiple Agents have been used to analyse the blogs by dividing the work areas and communicating themselves using Agent Communication Language (ACL) and FIPA.