SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

Aim. This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods. Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results. The results on ten runs of 10-fold crossvalidation show that our SOSPCNN model yields a sensitivity of 92.25 ± 2.19 , a specificity of 92.75 ± 2.49 , a precision of 92.79 ± 2.29 , an accuracy of 92.50 ± 1.18 , an F1 score of 92.48 ± 1.17 , an MCC of 85.06 ± 2.38 , an FMI of 92.50 ± 1.17 , and an AUC of 0.9587. Conclusion. The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.

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PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Frontiers in Public Health ◽

10.3389/fpubh.2021.768278 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shui-Hua Wang ◽

Ziquan Zhu ◽

Yu-Dong Zhang

Keyword(s):

Neural Network ◽

Infectious Disease ◽

Convolutional Neural Network ◽

Loss Function ◽

Data Augmentation ◽

State Of The Art ◽

Diagnosis System ◽

Backbone Network ◽

The Mean ◽

Better Than

Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system.Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions.Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI).Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

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PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6633755 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Shui-Hua Wang ◽

Yin Zhang ◽

Xiaochun Cheng ◽

Xin Zhang ◽

Yu-Dong Zhang

Keyword(s):

Neural Network ◽

Pulmonary Tuberculosis ◽

Healthy Subjects ◽

Data Augmentation ◽

State Of The Art ◽

Accurate Diagnosis ◽

Test Set ◽

The World ◽

Stochastic Pooling ◽

Better Than

Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

Intelligent Decision Technologies ◽

10.3233/idt-200192 ◽

2021 ◽

pp. 1-10

Author(s):

Gayatri Pattnaik ◽

Vimal K. Shrivastava ◽

K. Parvathi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Data Augmentation ◽

State Of The Art ◽

Deep Convolutional Neural Network ◽

Fine Tuning ◽

Tomato Plants ◽

Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

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Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling

Journal of Medical Systems ◽

10.1007/s10916-017-0845-x ◽

2017 ◽

Vol 42 (1) ◽

Cited By ~ 57

Author(s):

Shui-Hua Wang ◽

Yi-Ding Lv ◽

Yuxiu Sui ◽

Shuai Liu ◽

Su-Jing Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Stochastic Pooling

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An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

The Journal of Laryngology & Otology ◽

10.1017/s0022215120000444 ◽

2020 ◽

Vol 134 (4) ◽

pp. 328-331 ◽

Cited By ~ 1

Author(s):

P Parmar ◽

A-R Habib ◽

D Mendis ◽

A Daniel ◽

M Duvnjak ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Diagnostic Accuracy ◽

Convolutional Neural Network ◽

Middle Turbinate ◽

Concha Bullosa ◽

Computed Tomography Images ◽

Computed Tomography Scans ◽

Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

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An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

The Journal of Laryngology & Otology ◽

10.1017/s0022215119002536 ◽

2019 ◽

Vol 134 (1) ◽

pp. 52-55 ◽

Cited By ~ 3

Author(s):

J Huang ◽

A-R Habib ◽

D Mendis ◽

J Chong ◽

M Smith ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Sinus Surgery ◽

Computed Tomography Scans ◽

Ethmoidal Artery ◽

Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

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Classification of Alzheimer’s Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3001 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1040-1048 ◽

Cited By ~ 5

Author(s):

Xianwei Jiang ◽

Liang Chang ◽

Yu-Dong Zhang

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Convolutional Neural Network ◽

Data Augmentation ◽

State Of The Art ◽

Training Dataset ◽

Batch Normalization ◽

Heavy Burden

More than 35 million patients are suffering from Alzheimer’s disease and this number is growing, which puts a heavy burden on countries around the world. Early detection is of benefit, in which the deep learning can aid AD identification effectively and gain ideal results. A novel eight-layer convolutional neural network with batch normalization and dropout techniques for classification of Alzheimer’s disease was proposed. After data augmentation, the training dataset contained 7399 AD patient and 7399 HC subjects. Our eight-layer CNN-BN-DO-DA method yielded a sensitivity of 97.77%, a specificity of 97.76%, a precision of 97.79%, an accuracy of 97.76%, a F1 of 97.76%, and a MCC of 95.56% on the test set, which achieved the best performance in seven state-of-the-art approaches. The results strongly demonstrate that this method can effectively assist the clinical diagnosis of Alzheimer’s disease.

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Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

STEMedicine ◽

10.37175/stemedicine.v2i8.101 ◽

2021 ◽

Vol 2 (8) ◽

pp. e101

Author(s):

Jian Wang ◽

Dimas Lima

Keyword(s):

Neural Network ◽

Multiple Sclerosis ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Early Stage ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

White Matter Lesions ◽

Learning Methods ◽

Stochastic Pooling

Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.

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Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

10.1101/2020.03.13.990242 ◽

2020 ◽

Cited By ~ 12

Author(s):

Alejandro Lopez-Rincon ◽

Alberto Tonda ◽

Lucero Mendoza-Maldonado ◽

Daphne G.J.C. Mulders ◽

Richard Molenkamp ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Deep Learning ◽

Convolutional Neural Network ◽

State Of The Art ◽

Diagnostic Methods ◽

Neural Network Classifier ◽

Explainable Artificial Intelligence ◽

Primer Sets ◽

Virus Strains

ABSTRACTIn this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from available repositories, separating the genome of different virus strains from the Coronavirus family with considerable accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are first validated on samples from other repositories, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets on existing datasets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from NGDC, separating the genome of different virus strains from the Coronavirus family with accuracy 98.73%. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from NCBI and GISAID, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

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Automatic highlighting of the region of interest in computed tomography images of the lungs

Computer Optics ◽

10.18287/2412-6179-co-659 ◽

2020 ◽

Vol 44 (1) ◽

pp. 74-81 ◽

Cited By ~ 1

Author(s):

T.A. Pashina ◽

A.V. Gaidel ◽

P.M. Zelter ◽

A.V. Kapishnikov ◽

A.V. Nikonorov

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Region Of Interest ◽

Computed Tomography Images ◽

Otsu Method ◽

Automated Processing ◽

Medical University ◽

Better Than

This article discusses the creation of masks for highlighting the lungs in computed tomography images using three methods – the Otsu method, a simple convolutional neural network consisting of 10 identical layers, and the convolutional neural network U-Net. We perform a study and comparison of methods used for automatically highlighting the region of interest (ROI) in computed tomography images of the lungs, which were provided as a courtesy from the Clinics of Samara State Medical University. The solution to this problem is relevant, because medical workers have to manually select the ROI as the first step of the automated processing of lung CT images. An algorithm for post-processing images based on the search for contours, which allows one to improve the quality of segmentation, is proposed. It is concluded that the U-Net highlights the ROI relating to the lung better than the other two methods. At the same time, the simple convolutional neural network highlights the ROI with an accuracy of 97.5%, which is better than the accuracy of 96.7% of the Otsu method and 96.4% of the U-Net.

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