期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network 被引量:1
1
作者 v.ulagamuthalvi G.Kulanthaivel +1 位作者 A.Balasundaram Arun Kumar Sivaraman 《Computer Systems Science & Engineering》 SCIE EI 2022年第10期275-289,共15页
One of the fast-growing disease affecting women’s health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than m... One of the fast-growing disease affecting women’s health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately.Deep learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical images.Existing methods focused on using various conventional and machine learning methods for processing natural and medical images.It is inadequate for the image where the coarse structure matters most.Most of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in classification.Whereas deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate prediction.Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images.The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms. 展开更多
关键词 Medical image processing deep learning convolution neural network breast cancer feature extraction classification
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部