Lung Nodule Diagnosis on 3D Computed Tomography Images Using Deep Convolutional Neural Networks

Zhang, Qianqian; Wang, Haifeng; Yoon, Sang Won; Won, Daehan; Srihari, K.

doi:10.1016/j.promfg.2020.01.375

Cited by 30 publications

(21 citation statements)

References 29 publications

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“…El Asnaoui et al (2020) presented a comparison of recent DCNN architectures for automatic binary classification of pneumonia images based on fined tuned versions of VGG16 (Simonyan & Zisserman, 2014;Zhang et al, 2019), VGG19 (Simonyan & Zisserman, 2014;Zhang et al, 2019), DenseNet201 (Huang et al, 2017), Inception_ResNet_V2 (Szegedy et al, 2016), Inception_V3 (Szegedy et al, 2015), Resnet50 (He et al, 2016) and MobileNet_V2 (Sandler et al, 2018). The proposed work has been tested using chest X-ray & CT dataset.…”

Section: Related Workmentioning

confidence: 99%

Using X-ray images and deep learning for automated detection of coronavirus disease

Asnaoui

Youness

2020

Journal of Biomolecular Structure and Dynamics

281

239

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Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201). ARTICLE HISTORY

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Section: Related Workmentioning

confidence: 99%

Using X-ray images and deep learning for automated detection of coronavirus disease

Asnaoui

Youness

2020

Journal of Biomolecular Structure and Dynamics

281

239

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“…VGG16 and VGG19 [39] are CNN architectures aimed to attain high accuracy in significant image identification applications with very narrow convolution filters (3×3). The depth [40] of the max-pooling, convolution, and fully connected layers vary between two implementations: 16 layers in VGG16 and 19 layers in VGG19.…”

Section: Vgg16 and Vgg19mentioning

confidence: 99%

Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network

Zaki¹,

Narejo²,

Ahsan³

et al. 2021

IJACSA

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Agriculture on earth is the biggest need for human sustenance. Over years, many farming methods and components have become computerized to guarantee quicker production with higher quality. Because of the enlarged demand in the farming industry, agricultural produce must be cultivated using an efficient process. Onion (Allium cepa L.) is an economically valuable crop and is the second-largest vegetable crop in the world. The spread of various diseases highly affected the production of the onion crop. One of the serious and most common diseases of onion worldwide is purple blotch. To compensate for a limited amount of training dataset of healthy and infected onion crops, the proposed method employs a pretrained enhanced InceptionV3 model. The proposed model detects onion disease (purple blotch) from images by recognizing the abnormalities caused by the disease. The suggested approach achieves a classification accuracy of 85.47% in recognizing the disease. This research investigates a novel approach for the rapid and accurate diagnosis of plant/crop diseases, laying the theoretical foundation for the use of deep learning in agricultural information.

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“…To compare and correlate its performance with the top layer of the following DL architectures (CNN, ReLU, Sigmoid, Softmax Regression function), on analysis and Transfer Learning approach [5][6][7] the lack of a data set for medical imagery is great.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

Naralasetti¹,

Shaik²,

Katepalli³

et al. 2021

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Diagnosis based on chest X-rays is widely used and approved for the diagnosis of various diseases such as Pneumonia. Manually screening of theses X-ray images technician or radiologist involves expertise and time consuming. Addressing this, we propose an automated approach for the diagnosis of pneumonia by assisting doctors in spotting infected areas in the X-ray images. We propose a deep Convolutional Neural Network (CNN) model for efficiently detecting the presence of pneumonia in the X-ray images. The proposed CNN is designed with 5 convolution blocks followed by 4 fully connected layers. In order to boost the performance of the model, we incorporate batch normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along with Adam optimizer and binary Cross-Entropy loss function while training the model using back propagating algorithm. The proposed model is validated on two publicly accessible benchmark datasets, and the experimental studies conducted on these datasets indicate that the proposed model is efficient. The suggested CNN architecture with specified hyper parameters allows the model to outperform several existing models by achieving accuracy of 97.73% and 91.17% respectively for binary and multi-class classification tasks of pneumonia disease.

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Lung Nodule Diagnosis on 3D Computed Tomography Images Using Deep Convolutional Neural Networks

Cited by 30 publications

References 29 publications

Using X-ray images and deep learning for automated detection of coronavirus disease

Using X-ray images and deep learning for automated detection of coronavirus disease

Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network

Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

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