Abstract
Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.
Highlights
In 2018, more than 1.7 million people died from lung cancer, which accounted for the highest proportion of deaths of all kinds of diseases, reaching 18.4%
In our previous work [24], we proposed a multi-level convolutional neural network (ML-CNN), which consists of three levels two-layer CNNs, and achieved an accuracy of 84.81%
We introduce how to select an efficient configuration from diverse structures and hyper parameters based on ML-xResNet
Summary
In 2018, more than 1.7 million people died from lung cancer, which accounted for the highest proportion of deaths of all kinds of diseases, reaching 18.4%. Lung cancer has the highest death rate (26%) among the top four major cancers with a low five-year survival rate of 18%, according to the report from American Cancer Society in 2019 [2]. A lung nodule is an abnormal growth on the lung that is smaller than 30 mm [4] If it is larger than 30 mm, it is called lung mass and has a higher chance of being cancerous [5]. Malignant nodules can spread to other organs or tissues and proliferate quickly,
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