Convolutional neural networks (CNN), due to its self-learning ability and deep feature extraction capability has created a new era to the biomedical applications and medical image analysis. Computer aided diagnosis system (CAD) using CNN’s helps radiologists in decision making and reduces their difficulty on screening lung nodules. This paper presents CAD system using pre-trained networks AlexNet, VGG16 and a new network is designed based on the inference obtained to eliminate the drawbacks of available network for the early prediction of lung cancer. Comparative analysis based on the performance parameters proved that the proposed CNN architecture outperforms the pre-trained networks. Experiments were evaluated using computed tomography (CT) scan images obtained from LIDC-IDRI dataset.
Paper
Full text
Lung Nodule Classification in CT Images Using Convolutional Neural Network
Semantic Scholar · Computer Science · 2019
Abstract
Convolutional neural networks (CNN), due to its self-learning ability and deep feature extraction capability has created a new era to the biomedical applications and medical image analysis. Computer aided diagnosis system (CAD) using CNN’s helps radiologists in decision making and reduces their difficulty on screening lung nodules. This paper presents CAD system using pre-trained networks AlexNet, VGG16 and a new network is designed based on the inference obtained to eliminate the drawbacks of available network for the early prediction of lung cancer. Comparative analysis based on the performance parameters proved that the proposed CNN architecture outperforms the pre-trained networks. Experiments were evaluated using computed tomography (CT) scan images obtained from LIDC-IDRI dataset.