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In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model.
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans.
SOTA for Lung Nodule Classification on LIDC-IDRI (Accuracy(10-fold) metric )
Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN.
The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.
Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thoracic Computed Tomography (CT) and nodule location.
In this paper, we propose Fusion classifier in conjunction with the cascaded convolutional neural network models.
In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans.
TPE or random search was used for parameter optimization of SVM and XGBoost.
Lung nodule classification is a class imbalanced problem because nodules are found with much lower frequency than non-nodules.