Using Machine Learning To Detect and Prevent Early Stages of Skin Cancer in Underrepresented Communities
ISEF · 2022 Translational Medical Science
Overview
Artificial intelligence is becoming a more widely utilized technology in the healthcare industry to improve predictability and consistency. However, this powerful technology does come with some limitations, one being the datasets used usually neglect people of color. This is problematic because the people most affected by skin disease are those of color. Skin cancer in these communities is often detected in the late stages. This is caused by skin tone, shortage of dermatologists, and cost of skin checks. The model I developed had an overall accuracy of 69.33%. The accuracy my model produced is extremely promising and has the potential to save lives. The capabilities of artificial intelligence could revolutionize the dermatology industry by making it more accessible for people to get tested for skin diseases.
Competition history
- ISEF 2022
Resources
Related projects
ISEF · 2023
DermaSkan: Using Convolutional Neural Networks to Detect and Prevent Skin Cancer in Underrepresented Communities by Using an Android App
ISEF · 2024
Improving Racial Equity in Skin Cancer Detection: Using Artificial Intelligence Driven Synthetic Image Generation and Cascading Convolutional Neural Networks to Diagnose Cancer in Lesions of Varying Skin Tones
ISEF · 2022
DermaTech: A Novel, Non-Invasive Technology To Detect Skin Cancer
ISEF · 2017
Utilizing Machine Learning Techniques to Identify Cancerous Skin Lesions
ISEF · 2024
Detect Early Melanoma Cancer Using Machine Learning
ISEF · 2022
A Multi-Output Convolutional Neural Network Model for Melanoma Detection and Prevention
ISEF · 2018
Machine Learning Approach to Computer Assisted Diagnosis of Skin Diseases
ISEF · 2024
Rapid Screening for Major Skin Cancers with M-SCAHN: Multimodal Hierarchical CNN-Transformer Hybrid Networks with Advanced Interpretability, Lesion Evolution Tracking, Trait Identification, and Color Constancy for Improved Generalization in Diverse Populations
Closest projects by meaning, across every fair and year in the corpus.
Browse more like this
Source: Regeneron International Science and Engineering Fair