Multi-path convolutional neural network for lung cancer detection

• Published in: Multidimensional systems and signal processing Vol. 30; no. 4; pp. 1749 - 1768
• Main Authors: Sori, Worku Jifara, Feng, Jiang, Liu, Shaohui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Circuits and Systems; Computed tomography; Computer vision; Electrical Engineering; Engineering; Lung cancer; Medical screening; Morphology; Nodules; Retraining; Signal,Image and Speech Processing; Size effects; Medical image; CNN’s; Lung cancer; Image detection
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-018-0626-9

Lung cancer is the leading cause of death among cancer-related death. Like other cancers, the finest solution for lung cancer diagnosis and treatment is early screening. Automatic CAD system of lung cancer screening from Computed Tomography scan mainly involves two steps: detect all suspicious pulmonary nodules and evaluate the malignancy of the nodules. Recently, there are many works about the first step, but rare about the second step. Since the presence of pulmonary nodules does not absolutely specify cancer, the morphology of nodules such as shape, size, and contextual information has a sophisticated relationship with cancer, the screening of lung cancer needs a careful investigation on each suspicious nodule and integration of information of all nodules. We propose deep CNN architecture which differs from those traditionally used in computer vision to solve this problem. First, the suspicious nodules are generated with the modified version of U-Net and then the generated nodules become an input data for our model. The proposed model is a multi-path CNN which exploits both local features as well as more global contextual features simultaneously to automatically detect lung cancer. To this end, the model used three paths, each path employed different receptive field size which helps to model distant dependencies (short and long-range dependencies of the neighboring pixels). Then, to further upgrade our model performance, we concatenate features from the three paths. This balance the receptive field size effect and makes our model more adaptable to the variability of shape, size, and contextual information among nodules. Finally, we also introduce a retraining phase system that permits us to tackle difficulties related to the imbalance of image labels. Experimental results on Kaggle Data Science Bowl 2017 challenge shows that our model is better adaptable to the described inconsistency among nodules size and shape, and also obtained better detection results compared to the recently published state of the art methods.