卷积神经网络
肺癌
人工智能
深度学习
计算机科学
计算机断层摄影术
模式识别(心理学)
召回
放射科
医学
病理
语言学
哲学
作者
Muntasir Mamun,Md Ishtyaq Mahmud,Mahabuba Meherin,Ahmed Abdelgawad
标识
DOI:10.1109/spin57001.2023.10116075
摘要
The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a deep learning modelbased Convolutional Neural Network (CNN) framework for the early detection of lung cancer using CT scan images. We also have analyzed other models for instance Inception V3, Xception, and ResNet-50 models to compare with our proposed model. We compared our models with each other considering the metrics of accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the model's performance, we observed that CNN outperformed other models and has been shown to be promising compared to traditional methods. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.
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