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Chinmay Dharmik

Chinmay Dharmik

AI & Machine Learning engineer specializing in medical image analysis

kyrotron
Adelaide, South Australia, Australia
Joined February 2026
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Summary

Published researcher in medical image analysis and applied deep learning: contributed as co-author to peer-reviewed papers on automated diagnosis from ultrasound and systematic reviews on AI for COVID-19 detection. mdpi+2
Practitioner of machine learning and computer vision with industry and internship experience: profiles list roles spanning machine learning engineering, computer vision internships, and short-term consulting engagements. weekday+1
Academic foundation in AI and engineering: holds a master’s degree in Artificial Intelligence and Machine Learning from the University of Adelaide and a B.Tech in Instrumentation and Control Engineering from Manipal Institute of Technology. orcid+2
Skills span research and applied tooling: public profiles associate him with AI, large language models, electronics, MATLAB, Arduino and statistics, reflecting both research and prototyping capabilities. weekday

Work

Education

Writing

Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques

April 1, 2022

Developed an automated computer-aided diagnostic (CAD) tool to discriminate hypertrophic cardiomyopathy (HCM) from healthy subjects on ultrasound images using local directional pattern features combined with deep (ResNet-50) features and an integrated diagnostic index.

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Role of Artificial Intelligence in COVID-19 Detection

December 1, 2021

Systematic review of state-of-the-art AI techniques applied to X-ray, CT, and ultrasound imaging modalities for COVID-19 detection, discussing approaches, challenges, and future trends for AI systems in disease detection during the pandemic.

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Local Preserving Class Separation Framework to Identify Gestational Diabetes Mellitus Mother Using Ultrasound Fetal Cardiac Image

January 1, 2020

Proposed a local preserving class separation (LPCS) framework and shearlet-based texture features to detect fetal myocardial hypertrophy associated with gestational diabetes mellitus using ultrasound fetal cardiac images; reported high classification accuracy using SVM.

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