Malu G

Malu G

Assistant Professor

Dr. Malu G. is a faculty member in the School of Computer Science and Engineering (SoCSE). She holds a Ph.D. in Computer Science and Technology and a Master’s degree in Computer Applications from the University of Kerala, along with a Postdoctoral Fellowship from the Indian Institute of Information Technology and Management-Kerala. With over nineteen years of experience in teaching and research, Dr. Malu specializes in Pattern Recognition, Medical Image Processing, Shape and Margin Descriptors, and Graph Algorithms. Her research interests focus on the development of new sophisticated methodologies and algorithms in these areas for the betterment of people and society.

Furthermore, she has contributed to the early diagnosis of breast cancer in collaboration with the Regional Cancer Centre (RCC), Trivandrum, as well as the diagnosis of Neurodegenerative diseases such as Alzheimer’s, Mild Cognitive Impairment, and Hydrocephalus using structural descriptors, among other projects.

Dr. Malu also plays a crucial role in the digital transition of intangible cultural art forms of Kerala. She serves as the Principal Investigator of the Centre for Digital Transformation in Culture. The centre aims to enhance the livelihood of artists and promote art forms of Kerala and beyond through Intelligent Digital Systems.

Dr. Malu has made significant contributions to her field, as evidenced by an Indian patent entitled ‘An automated lesion detection system for DCE-MRI using circular mesh-based shape and margin descriptor. She is a recipient of the SPEED-IT research fellowship and actively contributes to various central and state government projects.

Selected Publications:

  1. Malu, G., Sherly Elizabeth, and Sumod Mathew Koshy. “Circular mesh-based shape and margin descriptor for object detection.” Pattern Recognition84 (2018): 97-111.
  2. Sreelakshmi, S., et al. “M-Net: An encoder-decoder architecture for medical image analysis using ensemble learning.” Results in Engineering17 (2023): 100927.
  3. Rudhra, B., et al. “A Novel deep learning approach for the automated diagnosis of normal pressure hydrocephalus.” Journal of Intelligent & Fuzzy Systems5 (2021): 5299-5307.
  4. Malu, G., Elizabeth Sherly, and Sumod Mathew Koshy. “An automated algorithm for lesion identification in dynamic contrast enhanced MRI.” International Journal of Computer Applications in Technology1 (2015): 23-30.
  5. Raj, Sini S., et al. “A deep approach to quantify iron accumulation in the DGM structures of the brain in degenerative Parkinsonian disorders using automated segmentation algorithm.” 2019 International Conference on Advances in Computing, Communication and Control (ICAC3). IEEE, 2019.