Abstract
Lung cancer is among the world's worst cancers, and accounted for 27%of all cancers in 2018. Despite substantial improvement in recent diagnoses and medications, the five year cure ratio is just 19%. Before even the diagnosis, classification of lung nodule is an essential step, particularly because early detection can help doctors with a highly valued opinion. CT image detection and classification is possible easily and accurately with advanced vision devices and machine-learning technology. This field of work has been extremely successful. Researchers have already attempted to improve the accuracy of CAD structures by computational tomography (CT) in the screening of lung cancer in several deep learning models. In this paper, we proposed a fully automated lung CT system for lung nodule classification, namely, new transfer method (NTM) which has two parts. First features are extracted by applying different VOI and feature extraction techniques. We used intensity, shape, contrast of border and spicula extraction to extract the lung nodule. Then these nodules are transfer to the classification part where we used advance-fully convolution network (A-FCN) to classify the lung nodule between benign and malignant. Our A- FCN network contain three types of layers that helps to enhance the performance and accuracy of NTM network which are convolution layer, pooling layer and fully connected layer. The proposed model is trained on LIDC-IDRI dataset and attained an accuracy of 89.90 % with AUC of 0.9485.
Highlights
In this modern era of machine learning, doctors are finding some form of support that encourages their ability to analyze and diagnose Computed tomography (CT) images of patients and to identify extremely effective and accurate pathologies
In order to obtain a suspicious-sensitive classification in CT images, we have to address at minimum two significant obstacles, the complexity of nodule depiction induced by a broad variety of nodule's morphology variants, and the problem raised by analytical models' radiological complexity to identify qualitative features as it is difficult to differentiate benign nodules from malignant nodules
The network is divided into two parts: (1) volume of interest (VOI) extraction and feature extraction; (2) advance-fully convolutional network (A-FCN)
Summary
In this modern era of machine learning, doctors are finding some form of support that encourages their ability to analyze and diagnose CT images of patients and to identify extremely effective and accurate pathologies. In order to obtain a suspicious-sensitive classification in CT images, we have to address at minimum two significant obstacles, the complexity of nodule depiction induced by a broad variety of nodule's morphology variants, and the problem raised by analytical models' radiological complexity to identify qualitative features as it is difficult to differentiate benign nodules from malignant nodules. For lung nodule classification task we proposed advance-fully convolution network (A-FCN)
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