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Lung Nodule Classification on Computed Tomography Images Using Fractalnet
Wireless Personal Communications ( IF 2.2 ) Pub Date : 2021-02-19 , DOI: 10.1007/s11277-021-08258-w
Amrita Naik , Damodar Reddy Edla , Venkatanareshbabu Kuppili

Lung Cancer is the most common cancer all over the world and is mostly diagnosed at later stages, thus increasing the risk of death. For early detection of malignant nodules there is a need for automated nodule detection system using imaging modalities like CT scan of lungs. Several machine learning algorithm specially deep learning algorithm have been used to accomplish this task, but without guaranteed accuracy. In the proposed work, we intend to improve the accuracy of pulmonary nodule classification system using fractalnet architecture. Fractalnet is also compared with other deep learning architectures and an elaborative discussion on the same is also mentioned in this paper. We have validated the classification of lung nodules on LUNA dataset and have achieved an accuracy, specificity, sensitivity, area under receiver operating characteristic curve score of 94.7%, 90.41%, 96.68%, 0.98 respectively using fractalnet architecture, which is a substantial improvement over previous works in literature.



中文翻译:

使用Fractalnet对计算机断层扫描图像进行肺结节分类

肺癌是全世界最常见的癌症,大多在后期被诊断出来,因此增加了死亡风险。为了早期检测恶性结节,需要使用诸如肺部CT扫描的成像方式的自动结节检测系统。几种机器学习算法,特别是深度学习算法已被用来完成此任务,但并不能保证准确性。在拟议的工作中,我们打算使用分形网络架构提高肺结节分类系统的准确性。Fractalnet还与其他深度学习架构进行了比较,并且本文中还对它进行了详尽的讨论。我们已经验证了LUNA数据集上肺结节的分类,并获得了准确性,特异性,敏感性,

更新日期:2021-02-19
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