Optimization of Pediatric Cancer Diagnosis with Convolutional Neural Networks (CNNs)

Research Project by Gifted Gabber Alumni - Simran Saluja

Project's Result

Published in the Journal of Student Research: Access the Full Paper Here

Gifted Gabber Research Program

This impactful research was conducted during the 2022 Research Program at Gifted Gabber, under the guidance of Research Professor Rajagopal Appavu and Mentor Coach Jo.

Research Summary

Accurate and early diagnosis is a critical factor in improving survival rates for pediatric cancer patients. Leveraging the power of Convolutional Neural Networks (CNNs), Simran Saluja’s research explores innovative ways to enhance diagnostic accuracy and optimize the detection process.

The study focuses on the application of CNNs—a type of deep learning algorithm—trained to identify pediatric cancer patterns in medical imaging with unprecedented precision. By analyzing thousands of images, the research demonstrates how CNNs can reduce diagnostic errors and assist healthcare professionals in making faster, more informed decisions.

Key findings highlight the potential of CNNs to significantly lower false-negative rates, improve image-based classification accuracy, and streamline diagnostic workflows in pediatric oncology. The research also discusses ethical considerations and the importance of integrating AI systems into healthcare settings in a manner that complements, rather than replaces, human expertise.

Simran Saluja’s work stands as a testament to the transformative power of artificial intelligence in medical research and the impact of interdisciplinary mentorship at Gifted Gabber. Her research represents a meaningful step toward improving pediatric cancer care and sets a strong foundation for future advancements in AI-driven healthcare solutions.