Atrous convolution aided integrated framework for lung nodule segmentation and classification. (April 2023)

• Record Type: Journal Article
• Authors: Halder, Amitava; Dey, Debangshu
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Atrous convolution -- Dilated convolution -- Deep learning -- Lung Cancer -- Nodule characterization -- CADx; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104527
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Lung cancer has been recognized as the most life-threatening cancer all over the world. Appropriate detection of lung nodule using Computed Tomography (CT) images helps in early stage recognition of lung cancer. Different computer-aided algorithms play an important role in the early diagnosis of lung cancer and can increase the five-year survival rate of lung cancer patients. However, due to structural similarity, manually recognizing the malignant nodule from the benign is time-consuming and challenging task. Recently different deep learning (DL) based Computer-aided diagnosis (CADx) systems have been developed for lung nodule characterization. In this work, an integrated nodule segmentation and characterization framework has been developed using the concept of atrous convolution. The proposed Atrous Convolution-based Convolutional Neural Network (ATCNN) framework can segment and characterize lung nodules by capturing multi-scale features from the HRCT images. Different variants of the ATCNN framework have been analyzed for lung nodule characterization. Among them, ATCNN with a two-layer atrous pyramid and residual connections (ATCNN2PR) has demonstrated the highest classification performance indices for nodule characterization. The new ATCNN2PR framework has obtained an average Dice Similarity Coefficient (DSC), Jaccard Index (JI), and Boundary F1 (BF) score of 0.9715, 0.9520, and 0.9584 for nodule segmentation and sensitivity, specificity, accuracy of 95.84%, 96.89%, and 95.97% for lung nodule characterization on LIDC-IDRI dataset. The proposed automatic trainable end-to-end system has outperforms other competing frameworks by capturing multi-scale features from High-Resolution Computed Tomography (HRCT) nodule images.