European Journal of Molecular & Clinical Medicine

Abstract

An earlier detection and diagnosis of lung cancer requires a major task known as lung nodule candidate classification. To detect the lung nodule candidate, a Multi-Resolution 3- Dimensional Convolutional Neural Network and Knowledge Transfer (MR3DCNN-KT) model has been designed that can extract the contextual information between multiple samples of lung nodule image for increasing the detection accuracy. But, this model was not able to classify few types of nodules that may cause the false detection. Also, the training data preparation was high difficult due to the manual labeling that consumes more time and the label mistakes were introduced while using large scale datasets since 3D-CNN requires more number of samples. Hence this article proposes an Iteratively Optimized MR3DCNN-KT (IO-MR3DCNN-KT) model that establishes automated weak label initialization to classify the large scale lung nodule image datasets. This model is trained on dynamically updated training datasets in an iterative manner. A Fast and Automatic Weak Labeling(FAWL) scheme is applied to generate the initial training dataset. Nonetheless, the computational complexity of 3D-CNN structure is extremely high since it requires the significant number of computational resources. As a result, an IO-MR3D Depthwise Separable CNN and KT (IO-MR3D-DSCNN-KT) model is proposed that introduces the bottleneck-based 3D-DSCNN structure to reduce the computational complexity. This model can extract both spatial and temporal features using basic depthwise convolution (𝐜𝐨𝐧𝐯)and pointwise 𝐜𝐨𝐧𝐯, accordingly. Based on this model, the number of parameters used in the 3D-CNN structure is significantly reduced to automatically classify the lung nodule candidates. Finally, the experimental outcomes show that the IO-MR3D-DSCNNKT model promises increased accuracy and robustness compared to the IO-MR3DCNNKT and MR3DCNN-KT models.