Student Research · AI and Computer Science

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

Mentored by Rajagopal Appavu · with Coach Jo

AI and Computer Science November 2022 Published in Journal of Student Research
Abstract

In today’s world, technology has become much more prevalent in the world of medicine. Growing fields like biotechnology and artificial intelligence are helping save and improve lives in ways that we couldn’t have imagined just 40, 50 years ago. Some of the most common examples of this today are prosthetics and using artificial intelligence in radiology. In the past few years, artificial intelligence technology has been advancing and scientists have begun to research whether deep learning algorithms like convolutional neural networks can be used to help detect signs of and diagnose cancer. Specifically, a growing research field refers to using deep learning and CNN models to detect pediatric cancer, one of the hardest cancers to detect based on symptoms. In this paper, it will be discussed whether deep learning algorithms are effective in use for the detection or diagnosis of pediatric cancers.

Cite this work

Citation

Saluja, S. (2022). Optimization of Pediatric Cancer Diagnosis with Convolutional Neural Networks (CNNs). Gifted Gabber Research Archive. https://www.giftedgabber.com/paper/optimization-pediatric-cancer-diagnosis-convolutional-neural-saluja
Read the full paper

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

About the author

Student researcher

S
Simran Saluja
Gifted Gabber Research Program

Completed through the 2022 Research Program at Gifted Gabber.

Original publication

Published in Journal of Student Research

Vol. 11 No. 4 (2022)

These links open archived snapshots — JSR's live site is currently unstable, so we route through the Internet Archive's Wayback Machine for reliable access to the original publication.

← Browse the full research archive