An efficient IISH-2D DCNN-based lung nodule classification using CT scan images

Abstract

Lung cancer has been identified as the world’s leading cause of death. Precise detection and testing of lung nodules at an early stage are essential for the diagnosis of lung cancer, therefore, it is one of the best ways to prevent deaths of lung cancer. An automated nodule detection system provides a second opinion to radiologists during early diagnosis. Much existing research uses Deep Convolutional Networks (DCNNs) for lung nodules classification. However, DCNNs normally need careful tuning of hyperparameters to reveal their excellent performance. Although, with the increasing size of state-of-the-art convolutional neural networks, the evaluation cost of the traditional optimization algorithms has become deplorable in most cases. Also, Lung CT scans data have a data imbalance problem inherently. So, to solve those problems, this paper proposes IISH-2D DCNN for lung nodule classification. The proposed methodology consists of pre-processing and classification phases. In the pre-processing phase, the 3D-CT scan slice is converted into a 2D-slice, and then the nodule boundary is extracted by calculation of ROI. After that, the extracted boundary is given as input to the IISH-2D DCNN that classifies the lung nodules. The performance of the proposed methodology is compared to the existing works based on accuracy, sensitivity, and specificity metrics. Thus, the proposed model outperforms existing lung nodule classification methodologies with higher accuracy, sensitivity, and specificity that are 99.8%, 97%, and 99%, respectively. Also, the proposed methodology has fewer errors than the state-of-the-art methods. Hence, the suggested method attains better performance in lung nodule classification and proves to be more effective.