Abstract
Lung cancer has the highest mortality rate among all types of cancers. Early detection of lung cancer may improve survival rates. The two categories of pulmonary lung nodules have high visual similarities. So, distinguishing them is a challenging task for radiologists. The main purpose of this work is to use convolutional neural network to perform binary classification of pulmonary nodules in CT images. This paper proposes a new multi-scale (64 \(\times \) 64, 32 \(\times \) 32 and 16 \(\times \) 16) convolutional neural network architecture for benign and malignant nodules classification. In addition, transfer learning method is used to initialize the weights of multi-scale architecture. Experimental results on the dataset LIDC-IDRI demonstrate that the proposed method achieved accuracy of 93.88%, sensitivity of 93.36% and specificity of 93.26% on nodule malignancy classification. The proposed method also outperforms the other state-of-the-art methods explicitly designed for malignancy classification of pulmonary lung nodules.
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Data availibility statement
The datasets used in the current study are available in the below https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.
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Sakshiwala, Singh, M.P. A new framework for multi-scale CNN-based malignancy classification of pulmonary lung nodules. J Ambient Intell Human Comput 14, 4675–4683 (2023). https://doi.org/10.1007/s12652-022-04368-w
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DOI: https://doi.org/10.1007/s12652-022-04368-w