年份 | 2018 |
学科 | 系统软件 Systems Software |
国家/州 | United State of America |
Development of a Computer Aided System for the Classification of Breast Lesions from Mammogram Scans
Over the past few decades, Computer Aided Diagnosis systems have grown in popularity to improve mammogram performance. Currently, most systems do not suggest diagnosis; rather, a majority of systems only highlight suspicious lesions. This project aims to take performance a step further, using a convolutional neural network trained on images from USF’s Digital Database for Screening Mammography to classify breast lesions cropped from mammogram scans and a support vector machine trained off of written patient information to assess risk factor. The support vector machine (SVM) was created and optimized using a genetic algorithm with data gathered from UCI’s Mammography Database. Next, the convolutional neural network (CNN) was constructed in use for the classification of breast masses gathered from the mammogram scans. To increase the dataset size, each image was rotated and noise filters were added. Further preprocessing involved resizing the scans. Images were then shuffled randomly and passed through the convolutional neural network for training. The weights from the CNN and SVM were then saved to allow the system to work inside of a web application. Thus, allowing radiologists to save patient information for the support vector machine to assess and upload patient scans for the convolutional neural network to classify. The web application was hosted on a Python Flask back end and all user data was saved to a SQL database. This research will hopefully support the application of machine learning in future practices. Following the submission of this abstract, further research is to come.
高中生科研 英特尔 Intel ISEF
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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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· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·
The study or development of software, information processes or methodologies to demonstrate, analyze, or control a process/solution.
Algorithms (ALG): The study or creation of algorithms - step-by-step procedure of calculations to complete a specific task in data processing, automated reasoning and computing.
Cybersecurity (CYB): Studies involving the protection of a computer or computer system against unauthorized access or attacks. This can include studies involving hardware, network, software, host or multimedia security.
Databases (DAT): Studies that create or analyze data organization for ease of access, management and update.
Human/Machine Interface (HMC): Software application that presents information to a user about the state of a process and to accept and implement the operator’s control instructions.
Languages and Operating Systems (LNG): Studies that involve the development or analysis of artificial languages used to write instructions that can be translated into machine language and then executed by a computer or system software responsible for the direct control and management of hardware and basic system operations of a computer or mobile device.
Mobile Apps (APP): A study involving a software application developed specifically for use on small, wireless computing devices. These studies may include front-end development techniques, such as user interface design and cross-platform support, and/or back-end development techniques, such as data services and business logic.
Online Learning (LRN): Studies that focus on utilizing electronic technologies to access educational curriculum outside of a traditional means. Studies explore the design of learning activities and programs with online technologies, as well as the effective use of e-learning systems.
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.
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