基于AlexNet的乳腺癌组织病理学图像分类

A. Titoriya, Shelly Sachdeva
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引用次数: 25

摘要

深度学习在图像分类、目标检测等领域取得了优异的成绩。近年来,许多研究者尝试将深度学习应用于医学图像分析。卷积神经网络(Convolutional Neural Network, CNN)已经成为该领域中一个意义深远的模型。它是一种深度学习模型,它提取图像的特征并对其进行分类。在本研究中,使用著名的CNN架构“AlexNet”对乳腺癌(BC)组织病理学图像进行分析。组织病理学图像是乳腺癌诊断的金标准。在不久的将来,使用深度学习来预测乳腺癌将被证明是非常有效的。这里使用了一个包含82名患者的7909张图像的数据集来训练我们的模型,然后成功地对图像进行了分类。使用这种方法也获得了令人印象深刻的结果和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breast Cancer Histopathology Image Classification using AlexNet
Deep learning has achieved high performance in many fields like image classification, object detection etc. Recently many researchers have tried to carry out deep learning in medical image analysis. Convolutional Neural Network (CNN) has been set as a profound class of models in this field. It is a deep learning model which extracts the features of an image and then classify it. In this study, an analysis of Breast Cancer (BC) histopathology images is done using famous CNN architecture “AlexNet”. Histopathology images are the gold measure for the breast cancer diagnosis. Using deep learning for predicting breast cancer can prove to be very much effective in near future. Here a dataset, which consists 7909 images of 82 patients is used to train our model and later the image is successfully being classified. Impressive results and analysis are also achieved using this approach.
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