Development of AI Models from Mammography Images with CNN for Early Detection of Breast Cancer

N. Nurbaiti, Eka Putra Syarif Hidayat, Khairil Anwar, Dudung Hermawan, Salman Izzuddin
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Abstract

Early detection of breast cancer with computer assistance has developed since two decades ago. Artificial intelligence using the convolutional neural network (CNN) method has successfully predicted mammography images with a high level of accuracy similar to human brain learning. The potential of AI models provides opportunities to spot breast cancer cases better. This research aims to develop AI models with CNN using the public DDSM dataset with a sample size of 1871, consisting of 1546 images for training and 325 images for testing. These AI models provided prediction results with different accuracy rate. Increasing the accuracy of the AI model can be done by improving the image quality before the modeling process, increasing the number of datasets, or carrying out a more profound iteration process so that the AI model with CNN can have a better level of accuracy.
利用 CNN 从乳房 X 射线照相图像中开发人工智能模型,用于早期检测乳腺癌
利用计算机辅助早期检测乳腺癌的技术早在二十年前就已发展起来。使用卷积神经网络(CNN)方法的人工智能已成功预测了乳房 X 射线照相图像,其准确性与人脑学习相似。人工智能模型的潜力为更好地发现乳腺癌病例提供了机会。本研究旨在利用样本量为 1871 个的公开 DDSM 数据集,包括 1546 幅用于训练的图像和 325 幅用于测试的图像,利用 CNN 开发人工智能模型。这些人工智能模型提供了不同准确率的预测结果。要提高人工智能模型的准确率,可以在建模前提高图像质量、增加数据集的数量或进行更深入的迭代,从而使使用 CNN 的人工智能模型具有更高的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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