胶囊
卷积神经网络
人工智能
肺癌
计算机科学
模式识别(心理学)
深度学习
计算机断层摄影术
人工神经网络
放射科
医学
病理
生物
植物
作者
A.R. Bushara,Raghavendra Kumar,S. S. Kumar
标识
DOI:10.1016/j.bspc.2023.104930
摘要
The five-year survival rate for lung cancer is among the lowest of all malignancies. Lung cancer possesses a high incidence of death per capita, therefore finding it early is crucial. To this end, Computed Tomography (CT) scans are often employed for the early identification of lung cancer, with clinical judgement serving as the current reference standard. Deep learning Convolutional Neural Networks (CNNs) have been used in end-to-end approaches for the detection of lung nodules. Capsule Networks are one of the numerous deep learning models that have been presented as a potential solution to the problems caused by the shortcomings of CNNs, such as the inability of CNNs to recognize fine-grained spatial correlations. As of now, capsule networks have shown to be effective in solving medical imaging challenges. To build on the previous work, Visual Geometry Group - Capsule Network (VGG-CapsNet) an innovative capsule network-based combination of VGG and Capsule Network is introduced. According to the findings, VGG-CapsNet is superior to using a basic capsule network, or a combination of CNN capsule networks, with a 0.980 AUC and a 98.61 % F1-Score, a precision of 99.07 %, a recall of 98.16 %, a specificity of 99.07 %, and an accuracy of 98.61 % for LIDC-IDRI datasets, and 98.14 % precision, 99.16 specificity, 98.07 % accuracy, 0.98 AUC and 98.14 % F1-Score for Kaggle datasets.
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