Doctoral Research Scholars
Completed
Dr. A. Bakiya - Thesis title: Analysis of Normal and Abnormal Electromyograms using Fractional Calculus and Computational Intelligence. July 2020
Dr. A. Paramasivam - Thesis title: Analysis of Normal and Abnormal Electrogastrograms using Machine Learning and Internet of Things. May 2019
Ongoing
S. Arokiya Sukanya - Development of Diagnostic Assistance Systems for Segmentation and Classification of Dental Radiographic Images.
Masters Students Supervised
S. Jenova Rani, Detection of Lesions in Pulmonary CT Images, 2021
T. Padma Pradha, Acquisition and Analysis of Normal and Abnormal Electroglottography Signals, 2020
M. Saranya, Segmentation of Brain Tumor in Magnetic Resonance Images using Swarm Intelligence algorithms, 2019
E. Rakshana, Design of Diagnostic Systems for Breast Cancer Using Deep Learning Techniques, 2019
K. Kathiravan, Diagnosis of Skin Melanoma using Dermoscopy Images, 2018
A. Lavanya, Classification of Electrohysterograms using Deep Learning Methods, 2018
R. Abinaya Sundari, Segmentation and Classification of Fractured and Non Fractured Vertebrae using Spine Images, 2018
A. Sahana, Development of Capacitive Sensor Array for Analyzing the Nonhomogenity in Biological Materials, 2017
R. Arivarasu, Design of Electrogastrogram Measurement System and Development of EGG Classifiers, 2016
M. Varundeep Raju, Feature Extraction and Classification of Normal, Amyotrophic Lateral Sclerosis and Myopathic Electromyograms, 2016
N. Priyadharshini, Modelling, Analysis and Control of Nano Acrylamide Polymerization Reaction for Development of Tissue Mimicking Phantoms, 2015
M. Tharini, Classification of Term/Preterm Electrohysterograms using Chaos Theory and Complexity Analysis, 2015
Lijin V Jacob, Finite Element based Design of a Capacitive Sensor for Classification of Normal and Abnormal Liver Tissue, 2014
K. Manickavasagam, Classification of Ring Form Trophozoites of Plasmodium Species in Thin Blood Smear Images , 2014