2017英特尔ISEF获奖作品摘要 计算生物与生物信息学 Intel Finalist Abstract Computational Biology and Bioinformatics

获奖作品基本信息

年份	2017
学科	计算生物与生物信息学 Computational Biology and Bioinformatics
国家/州	United States of America

获奖作品名称

A Non-Invasive Diagnosis Method for Eye Cancers Using Machine Learning Algorithms

获奖作品摘要

Eye cancer has affected over 500,000 people worldwide in the past decade. Approximately 10% of people with eye cancer will die because it is not diagnosed early enough, causing it to metastasize throughout the body. Fortunately, eye cancer can be treated successfully when it is detected early, and it has a 5-year survival rate of 75%. The aim of this project was to use image processing and machine learning algorithms to develop a system that can correctly categorize an eye tumor as intraocular melanoma, intraocular lymphoma, or retinoblastoma, which are the three most common forms of eye cancer. Image processing algorithms in MatLab were used to analyze the features of a tumor such as asymmetry, border, pigmentation, size, and entropy. Index values were analyzed statistically using a normal distribution curve to better understand the impact of each independent factor. Then, these features were used to program a type of machine learning algorithm called an artificial neural network that can diagnose eye cancer. These comprehensive steps resulted in an algorithm that can diagnose eye cancer with 98% accuracy and can function as preliminary cancer diagnosis. Overall, this program can be used to diagnose 3 different forms of eye cancer non-invasively, and it can potentially save the lives of thousands of people who die from this cancer each year.

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ISEF 简介

英特尔国际科学与工程大奖赛，简称 "ISEF"，由美国 Society for Science and the Public（科学和公共服务协会）主办，英特尔公司冠名赞助，是全球规模最大、等级最高的中学生的科研科创赛事。ISEF 的学术活动学科包括了所有数学、自然科学、工程的全部领域和部分社会科学。ISEF 素有全球青少年科学学术活动的“世界杯”之美誉，旨在鼓励学生团队协作，开拓创新，长期专一深入地研究自己感兴趣的课题。

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英特尔 ISEF 学术活动详细介绍

英特尔 ISEF 全程指导方案

· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·

学科简介：计算生物与生物信息学 Computational Biology and Bioinformatics

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling (MOD): Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD): The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology (EVO): A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience (NEU): A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology (PHA): A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics (GEN): The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other (OTH): Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.