Faculty:Faculty of Science and Engineering
School:Computing and Information Science
Areas of Expertise: Computer Science
Ashim is a lecturer and researcher primarily working in the areas of computer vision, medical image processing, machine learning and intelligent systems.
His broader research interests focus on applied artificial intelligence and machine learning, image processing, robotics and the implementation of AI in immersive technology.
Connect with Ashim on LinkedIn
Visit Ashim's ResearchGate profile
Ashim has a multidisciplinary background in medical image processing, pattern and shape recognition, classification, artificial intelligence, applied mathematics, and business management.
His doctoral research developed a lightweight system for the early detection of diabetic retinopathy using retinal image processing, decision-based classification, and artificial intelligence (AI).
As a Fellow of the Higher Education Authority, UK (FHEA), Ashim's research interest is in medical informatics and imaging to develop solutions in the health sectors, applied AI, smart data transformation, algorithms, deep reinforcement learning, and application of AI in immersive technology.
M.Z. Hossain (2016) 'Real-time mobile enabled scheme for virtual spectacle frame selection'.
BSc Data Science Degree Apprenticeship
van der Linde, I., Chakraborty, A., Sapkota, R. (2022) 'How People Look at Pictures: A Health and Wellbeing Perspective using Machine Learning'. £10k.
University of Greenwich consultancy project developing tech-driven business solutions for Reebok Gym, Canary Wharf, London, 2011.
Strategic advisory committee for implementation of AI to the fine arts and wellbeing, Mukto Arts CIC, London from 2019.
Chakraborty, A., Wilson, G., Cristina, L., Biba., M. (2022) 'An Optimised Morphological Image Processing Method suitable for the Early Detection of Diabetic Retinopathy'. In: IEEE 18th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania.
Chakraborty, A., Chik, D., Biba, M. and Hossain, M. (2017) 'A decision scheme based on adaptive morphological image processing for mobile detection of early-stage diabetic retinopathy'. In: 11th International Conference (IEEE) on Software, Knowledge, Information Management and Applications (SKIMA).
Hossen, M., Chik, D., Chakraborty, A. and Hossain, M. (2016) 'Real-time mobile enabled scheme for virtual spectacle frame selection'. In: 9th International Conference (IEEE) on Software, Knowledge, Information Management and Applications.
Hasan, M. M., Chakraborty, A. and Cirstea, S. (2022). 'A next-generation explainable AI-enabled (XAI) expert system for eye disease detection and risks stratification', 1st MTRC annual research conference, Anglia Ruskin University, Chelmsford, UK (poster presentation).
Chakraborty, A. (2018) 'Mobile enabled intelligent retinal image-based diagnosis', AI for Medical Informatics, Engineering and Devices, 28 June 2018, Chelmsford, UK (presentation).
Chakraborty, A., Chik, D., Biba, M. and Hossain, M.A. (2017) 'A decision scheme based on adaptive morphological image processing for mobile detection of early-stage diabetic retinopathy' (presentation).
Chakraborty, A., Chik, D. and Hossain, M. (2016) 'Mobile based decision support system for early stage of diabetic retinopathy'. 6th Annual FsT Research Conference, New and Emerging Research, Anglia Ruskin University, Chelmsford, UK (poster presentation).