Projects
Project 01
Integration of Clinical Data and Deep Learning Models for Predictive Diabetic Retinopathy Diagnosis.
Development of a portable fundus imaging device with integrated machine learning for automated Diabetic Retinopathy detection
Diabetes mellitus and diabetic retinopathy (DR) present significant global public health challenges. Currently, 10.5% of the world's adult population has diabetes, as reported by the International Diabetes Federation. DR is a leading cause of vision impairment and blindness, affecting 30% to 40% of diabetic individuals. A meta-analysis estimates that 103 million people currently suffer from DR, a figure projected to rise to 161 million by 2045. Early detection is crucial, as timely interventions can prevent up to 95% of vision loss cases. Unfortunately, access to affordable healthcare and specialized eye care services is limited in many low- and middle-income countries, where trained ophthalmologists and advanced imaging tools are often concentrated in urban areas.
My project addresses disparities in diabetic retinopathy (DR) screening by developing a portable fundus imaging device that integrates machine learning for automated DR detection. This innovative device captures high-quality retinal images and utilizes advanced deep learning algorithms to accurately classify the severity of DR. By training on a comprehensive dataset of annotated images, the project ensures high diagnostic accuracy and reliability. The goal is to enhance accessibility and efficiency in DR screening, particularly in underserved areas, thereby facilitating timely diagnosis.
The innovation in this research lies in combining portable retinal imaging technology with state-of-the-art deep learning algorithms for DR detection with a portable device designed for use in diverse environments, from clinics to remote areas
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Authored a research paper titled Integration of Clinical Data and Deep Learning Models for
Predictive Diabetic Retinopathy Diagnosis”. -
Paper presentation at ACMSEGA 2024, an International Conference on Advances in Communication, Medical Electronics and Smart Grid Automation,findings submitted to Scopus.Click here to view certificate
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Among the youngest participants to have a research paper selected for publication ina Scopus-indexed journal.
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Project selected as Grand Prize winner at IRIS National Fair.
Project Presentation
Project Poster
Project 02
Research Paper on Study of Biomarker-based approaches for early detection of Alzheimer's using Machine Learning.Paper published in Curieux Scientific Journal.
This project explores the use of machine learning (ML) models applied to diverse biomarkers—including neuroimaging, cerebrospinal fluid proteins, blood-based markers, EEG signals, and genetic data—for the early detection of Alzheimer’s disease.The study highlights the promise of multimodal models, which combine multiple biomarkers for greater reliability, while also addressing challenges such as data imbalance, limited external validation, and the need for diverse datasets. Ultimately, this approach has the potential to transform Alzheimer’s diagnosis by enabling timely intervention and improving patient outcomes.
Project 03
Project on Pneumonia Detection using Chest X-Rays
Designed a medical imaging project for pneumonia detection. The machine learning algorithm could analyze chest X-ray images and accurately classify patients as pneumonic or healthy. Leveraged image preprocessing and convolutional neural networks (CNNs) to enhance diagnostic precision.
Project 04
AquaNova
Developed at ChangeMakers Summer Camp 2025 at Indian Institute of Technology(IIT),Delhi
Delhi thrives on Yamuna,yet it is drowning in pollution endangering the city's health,culture and ecology.High influx of untreated municipal, industrial,and agricultural wastewater particularly near Najafgarh,Barapullah and Shahdara. AquaNova is an oxygenic photogranule-based system capable of biologically removing excess phosphates and nitrates from polluted water, improving river water quality. It has a two-stage system: Prefiltration for TSS removal, followed by Chlorella Vulgaris photobioreactor for phosphate and nitrate assimilation under photoautotrophic conditions..
Project 05
EcoCell-An App to streamline recycling of discarded batteries from electric vehicles(EV).
At the Technovation Girls Challenge 2023, our two member team pioneered the development of an innovative app, Ecocell, designed to encourage the recycling of lithium-ion batteries, particularly among owners of electric vehicles (EVs).Recognizing the growing need for responsible waste management in the era of electric vehicles, I partnered with Attero, a company dedicated to building a circular economy by recovering critical minerals like lithium, cobalt, and nickel.
With their support, our initiative successfully recycled 2 tons of lithium-ion batteries, demonstrating the impact a simple yet effective idea could have on reducing waste and conserving valuable resources. This initiative not only provided a practical solution to a pressing environmental issue but also helped establish the foundation for future projects aimed at promoting sustainability and responsible resource use.
Pitch Video for Technovation Girls Challenge
App Demonstration
Project 06
AI-Powered Cognitive Classroom for Inclusive, Equitable, and Accessible Learning
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Designed a project that leveraged speech recognition and personalized learning analytics to support students with diverse learning needs.
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Focused on enhancing accessibility and engagement for neurodivergent and differently abled learners through adaptive content delivery and assistive technologies.
Project 07
Founder, GreenSparks
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A platform to create awareness about environmental degradation resulting from deforestation, pollution,
Project 08
IRRIS-Intelligent Real Time Responsive Irrigation System
Developed an Arduino-based system that automatically detects and manages soil moisture without human intervention.The system monitors soil conditions to improve water conservation and promote advanced agricultural growth.