Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

Abstract

Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.

PET/CT Radiomics in Lung Cancer: An Overview

“…Deep Learning has recently shown great potential for computer-assisted diagnosis [38,40] and for prediction of response to therapy [41] in patients with lung cancer. A potential drawback of Deep Learning, however, is that the resulting features are not as easy to interpret as the hand-designed ones, nor readily linkable to clinically relevant image findings [42].…”

A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

“…Experimental results showed an accuracy rate of 87.5%. Huseyin et al [16] proposed two Convolutional Neural Network (CNN)-based models to diagnose lung cancer on CT images. They are named Straight 3D-CNN, and they adopted conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM.…”

“…For MobileNet, the width multiplier = 1 and the resolution multiplier = 224 achieved an accuracy of 94% and 93% for training and validation, respectively, outperforming other parameter settings for MobileNet. Furthermore, we also compared the performance of the adopted architectures (Inception-V3 and Mobilenet-V1) with a linear classifier, vanilla 3DCNN, 3D Googlenet, 3D-AlexNet, DFCNet [24], TumorNet [42], CMixNet [17], and straight and hybrid 3D CNN architectures [16]. Table 11 presents a summary of the accuracy, sensitivity, and specificity of all the architectures.…”

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

“…[22] 2019 87.5 Ciompi, Francesco et al [29] 2017 79.5 * Jakimovski, Goran et al [30] 2019 99.6 Lakshmanaprabu, S.K. et al [31] 2018 94.5 Liao, Fangzhou et al [23] 2019 81.4 Liu, Xinglong et al [33] 2017 90.3 * Masood, Anum et al [21] 2018 96.3 Nishio, Mizuho et al [34] 2018 68 Onishi, Yuya et al [35] 2018 81.7 Polat, Huseyin et al [36] 2019 91.8 Qiang, Yan et al [37] 2017 82.8 Rangaswamy et al [38] 2019 96 Sori, Worku Jifara et al [39] 2018 87.8 Wang, Shengping et al [40] 2018 84 Wang, Yang et al [25] 2019 87.3 Yuan, Jingjing et al [41] 2017 93.9 * Zhang, Chao et al [42] 2019 92 * (c)…”