scholarly journals Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism

2021 ◽  
Vol 5 (2) ◽  
pp. 78-89
Author(s):  
Khai Dinh Lai ◽  
Thuy Thanh Nguyen ◽  
Thai Hoang Le
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive ◽  
Lung Nodule ◽  
Input Image ◽  
Attention Mechanism ◽  
Feature Maps ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Triplet Loss

The development of Computer-aided diagnosis (CAD) systems for automatic lung nodule detection through thoracic computed tomography (CT) scans has been an active area of research in recent years. Lung Nodule Analysis 2016 (LUNA16 challenge) encourages researchers to suggest a variety of successful nodule detection algorithms based on two key stages (1) candidates detection, (2) false-positive reduction. In the scope of this paper, a new convolutional neural network (CNN) architecture is proposed to efficiently solve the second challenge of LUNA16. Specifically, we find that typical CNN models pay little attention to the characteristics of input data, in order to address this constraint, we apply the attention-mechanism: propose a technique to attach Squeeze and Excitation-Block (SE-Block) after each convolution layer of CNN to emphasize important feature maps related to the characteristics of the input image - forming Attention sub-Convnet. The new CNN architecture is suggested by connecting the Attention sub-Convnets. In addition, we also analyze the selection of triplet loss or softmax loss functions to boost the rating performance of the proposed CNN. From the study, this is agreed to select softmax loss during the CNN training phase and triplet loss for the testing phase. Our suggested CNN is used to minimize the number of redundant candidates in order to improve the efficiency of false-positive reduction with the LUNA database. The results obtained in comparison to the previous models indicate the feasibility of the proposed model.

Multi-view Convolutional Neural Network for lung nodule false positive reduction

2020 ◽  
Vol 162 ◽  
pp. 113017 ◽  
Author(s):  
Salsabil Amin El-Regaily ◽  
Mohammed Abdel Megeed Salem ◽  
Mohamed Hassan Abdel Aziz ◽  
Mohamed Ismail Roushdy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive ◽  
Lung Nodule ◽  
False Positive Reduction

Convolutional neural network-based PSO for lung nodule false positive reduction on CT images

2018 ◽  
Vol 162 ◽  
pp. 109-118 ◽  
Author(s):  
Giovanni Lucca França da Silva ◽  
Thales Levi Azevedo Valente ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Marcelo Gattass
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Positive ◽  
Lung Nodule ◽  
Ct Images ◽  
False Positive Reduction

scholarly journals Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 38 ◽  
Author(s):  
Incheol Kim ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Input Image ◽  
Visual Interpretation ◽  
Feature Maps ◽  
Novel Method ◽  
Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

scholarly journals A robust convolutional neural network for lung nodule detection in the presence of foreign bodies

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Schultheiss ◽  
Sebastian A. Schober ◽  
Marie Lodde ◽  
Jannis Bodden ◽  
Juliane Aichele ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Foreign Bodies ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection

scholarly journals Super-resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

2021 ◽  
Author(s):  
Lakpa Dorje Tamang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Super Resolution ◽  
Input Image ◽  
Feature Maps ◽  
Proposed Model ◽  
Reconstruction Performance

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves an exquisite reconstruction performance of ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), while providing compelling SR reconstruction time.

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