基于深度学习的皮肤癌优化检测

Reynatha Chrestella Amandara Pangsibidang, S. Tuba
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引用次数: 0

摘要

皮肤癌是由异常细胞的形成引起的,这些细胞可以攻击或扩散到身体的许多部位。皮肤癌的症状可能包括大小、形状和颜色不同的痣,边缘不规则,多种色调,有时瘙痒或出血。暴露在太阳的紫外线辐射下导致90%以上的皮肤癌。为了将癌症分类为恶性或良性,本研究概述了使用深度学习的皮肤癌分类系统的开发。这个系统将使用TensorFlow和Keras。该技术用于根据收集到的数据集中的图像对皮肤癌进行分类。部署后,确定创建的卷积二维层系统准确率为78%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization Detection of Skin Cancer using Deep Learning
Skin cancer is caused by the formation of abnormal cells that can assault or spread to numerous body sections. The skin cancer symptoms may include a mole with varying size, shape, and color, irregular edges, multiple hues, and sometimes, itching or bleeding. Exposure to the sun's UV radiation is attributed to more than 90 percent of known occurrences of Skin Cancer. In order to categorize cancer as malignant or benign, this study outlines the development of a classification system for skin cancer using deep learning. This system would use TensorFlow and Keras. The technique is used to classify skin cancer using the images from the data set that have been collected. After deployment, it was determined that the created Convolutional 2-D layer system was 78% accurate.
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