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

Eali Stephen Neal Joshua,Yung-Cheol Byun,Midhun Chakkravarthy,Debnath Bhattacharyya,Hassène Gritli

doi:10.1155/2021/6695518

Abstract

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.

Highlights

Lung cancer is the most commonly [1] discovered dangerous cells and one of the most perilous cancer tissues that lead to fatality among males in 2019
Oracic computed tomography (CT) creates a volume of pieces that may be regulated to reveal several volumetric pictures of physical structures in the bronchi. 2D convolution dismisses the 3D spatial size, indicating that it is incapable of making complete usage of the 3D condition pertinent [5] information, and 3D Convolutional neural networks (CNNs) can, definitely, be in harmony with this
Our goal is to check empirically the trouble of determining bronchi acnes captured through computed tomography (CT) in an end-to-end means making usage of the 3D convolutional neural network (CNN) effectively to perform a binary distinction [6] on CT pictures [7] from the Lung Image Database Consortium Journal of Healthcare Engineering picture variety (LUNA 16 Dataset)

Summary

Introduction

Lung cancer is the most commonly [1] discovered dangerous cells and one of the most perilous cancer tissues that lead to fatality among males in 2019. Bronchi cancer tissues have happened to be a primary risk to personal everyday lifestyle. Low-dose computed tomography (CT) is a valuable method for pinpointing lung cancer tissues [2] early. 2D convolution dismisses the 3D spatial size, indicating that it is incapable of making complete usage of the 3D condition pertinent [5] information, and 3D CNN can, definitely, be in harmony with this. Our goal is to check empirically the trouble of determining bronchi acnes captured through computed tomography (CT) in an end-to-end means making usage of the 3D convolutional neural network (CNN) effectively to perform a binary distinction [6] (benign and malignant) on CT pictures [7] from the Lung Image Database Consortium

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Conclusion