Abstract
Lung cancer is the most common cancer around the world, with the highest mortality rate. If the malignant tumors are diagnosed at an early stage, the patient’s survival rate can be improved. Early diagnosis is possible with the help of lung cancer screening using low-dose CT scans. Identifying the malignant nodules in CT scans is quite challenging at an early stage, and hence there is a need of machine learning architecture that can effectively identify malignant and benign lung nodule in lung CT scans. This study combines the deep features extracted from Alexnet and Resnet deep learning models to classify the malignant and non-malignant nodule in CT scan images. The proposed deep learning architecture was experimented on LUNA 16 dataset and achieved an accuracy, sensitivity, specificity, positive predictive value, and Area under Curve (AUC) score of 94.3%, 95.52%, 91.11%, 89.52%, and .96%, respectively.
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Naik, A., Edla, D.R., Dharavath, R. (2022). A Deep Feature Concatenation Approach for Lung Nodule Classification. In: Misra, R., Shyamasundar, R.K., Chaturvedi, A., Omer, R. (eds) Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021). ICMLBDA 2021. Lecture Notes in Networks and Systems, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-030-82469-3_19
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