A review of convolutional neural network-based computer-aided lung nodule detection system

Sekar Sari, Tole Sutikno, Indah Soesanti, Noor Akhmad Setiawan


Worldwide, lung cancer is the major cause of death and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, naïve bayes, k-nearest neighbor (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We hope that our research may help professional researchers and radiologists design more effective CAD systems for lung cancer detection.


Computer-aided detection; Convolutional neural network; False-positive reduction; Feature extraction; Lung cancer detection; Lung nodule detection

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DOI: http://doi.org/10.11591/ijai.v12.i3.pp1044-1061


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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