scholarly journals 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Yung-Cheol Byun
Keyword(s):  
Low Dose ◽  
Binary Classification ◽  
Lung Nodule ◽  
Fine Grained ◽  
Internal Working ◽  
Nodule Detection ◽  
Classification Tasks ◽  
3D Cnn ◽  
Nodule Classification ◽  
Fold Cross Validation

The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and the Gradient Class Activation has shown to be useful for tailoring classification tasks and localization interpretation for fine-grained features and visual explanation for the internal working. Gradient-weighted class activation plays a crucial role for clinicians and radiologists in terms of trusting and adopting the model. Practitioners not only rely on a model that can provide high precision but also really want to gain the respect of radiologists. So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy. We have conducted the binary classification (benign and malignant) on computed tomography (CT) images from the LUNA 16 database conglomerate and database image resource initiative. The results obtained are through the 10-fold cross-validation. Experimental results have shown that the proposed lightweight architecture achieved a superior classification accuracy of 97.17% on LUNA 16 dataset when compared with existing classification algorithms and low-dose CT scan images as well.

scholarly journals Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Cleverson Alex Leitão ◽  
Gabriel Lucca de Oliveira Salvador ◽  
Priscilla Tazoniero ◽  
Danny Warszawiak ◽  
Cristian Saievicz ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Ultralow Dose ◽  
Thermoluminescent Dosimeters ◽  
Significant Difference ◽  
Nodule Detection ◽  
The Difference ◽  
Conventional Ct

Background. The effects of dose reduction in lung nodule detection need better understanding. Purpose. To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation. Materials and Methods. A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal–Wallis and McNemar’s tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom. Results. There was no significant difference in lung nodules’ detection when comparing ULD and LD protocols ( p = 0.208 to p = 1.000 ), but there was a significant difference when comparing each one of those against CCT ( p < 0.001 ). The detection rate of nodules with CT attenuation values lower than −600 HU was also different when comparing all protocols against CCT ( p < 0.001 to p = 0.007 ). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv. Conclusion. There is no significant difference in simulated lung nodules’ detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

Lung nodule detection in low-dose and thin-slice computed tomography

2008 ◽  
Vol 38 (4) ◽  
pp. 525-534 ◽  
Author(s):  
A. Retico ◽  
P. Delogu ◽  
M.E. Fantacci ◽  
I. Gori ◽  
A. Preite Martinez
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Lung Nodule ◽  
Thin Slice ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Ultra-Low-Dose MDCT of the Chest: Influence on Automated Lung Nodule Detection

2008 ◽  
Vol 9 (2) ◽  
pp. 95 ◽  
Author(s):  
Ji Young Lee ◽  
Myung Jin Chung ◽  
Chin A Yi ◽  
Kyung Soo Lee
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Automated Lung Nodule Detection at Low-Dose CT: Preliminary Experience

2003 ◽  
Vol 4 (4) ◽  
pp. 211 ◽  
Author(s):  
Jin Mo Goo ◽  
Jeong Won Lee ◽  
Hyun Ju Lee ◽  
Seunghwan Kim ◽  
Jong Hyo Kim ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Low Dose Ct ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping

2020 ◽  
Vol 60 ◽  
pp. 101628 ◽  
Author(s):  
Yiming Lei ◽  
Yukun Tian ◽  
Hongming Shan ◽  
Junping Zhang ◽  
Ge Wang ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Ct Images ◽  
Low Dose Ct ◽  
Nodule Classification ◽  
Activation Mapping

Wavelet Compression of Low-Dose Chest CT Data: Effect on Lung Nodule Detection

Radiology ◽  
2003 ◽  
Vol 228 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Jane P. Ko ◽  
Henry Rusinek ◽  
David P. Naidich ◽  
Georgeann McGuinness ◽  
Ami N. Rubinowitz ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Chest Ct ◽  
Wavelet Compression ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Ct Data
Sign in / Sign up

Export Citation Format

Share Document