Convolutional Neural Network Transfer Learning Research Articles

An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images.

In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability.

Fault States Diagnosis of Marine Diesel Engine Valve Based on a Modified VGG16 Transfer Learning Method

The marine diesel engine is an important power machine for ships. Traditional machine learning methods for diesel engine fault diagnosis usually require a large amount of labeled training data, and the diagnosis performance may decline when encounters vibrational and environmental interference. A transfer learning convolutional neural network model based on VGG16 is introduced for diesel engine valve leakage fault diagnosis. The acquired diesel engine cylinder head vibration signal is first converted to time domain, frequency domain, and wavelet decomposition images. Secondly, the VGG16 deep convolutional neural network is pretrained using the ImageNet dataset. Subsequently, fine tuning the network based on the pretrained basic parameters and image enhancement methods. Finally, the well‐trained model is adopted to train and test the target dataset. In addition, the cosine annealing learning rate setting method is used to make the learning rate close to the global optimal solution. Experimental results show that the proposed method has higher accuracy and better robustness against noise with a small sample dataset than traditional methods and deep learning models. This study not only demonstrates a novel view for the diagnosis of marine diesel engine valve leakage, but also provides an applicable diagnosis method for other similar issues.

Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning.

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.

Classification of jackfruit and cempedak using convolutional neural network and transfer learning

Jackfruit (Artocarpus integer) and Cempedak (Artocarpus heterophyllus) are two different Southeast Asian fruit species from the same genus that are quite similar in their external appearance, therefore, sometimes difficult to be recognized visually by humans, especially in the form of pictures. Convolutional neural networks (CNN) and transfer learning can provide an excellent solution to recognize fruits, where the methods are known to be able to classify objects with high accuracy. In this study, several models were proposed and constructed to recognize the Jackfruit and Cempedak using a deep convolutional neural network (DCNN). We proposed our custom-made own CNN model and modify five transfer learning models on pre-trained VGG16, VGG19, Xception, ResNet50, and InceptionV3. The experiment used our own dataset and the result showed that the proposed CNN architecture was able to provide an accuracy between 89% to 93.67% compared to the other CNN transfer learning.

Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network

Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products.

A novel CNN ensemble framework for bearing surface defects classification based on transfer learning

Bearing surface defect detection and classification methods based on machine vision have been widely used in bearing quality inspection due to their high-speed, high-precision, and non-contact advantages. However, traditional machine vision algorithms have low reusability and their development processes are often expensive and time consuming. Several deep learning-based bearing surface defect detection methods have been proposed. However, these deep learning models often require a large number of datasets, which is often difficult to achieve in the actual industry. Transfer learning provides a promising solution to the small sample difficulties associated with deep learning. However, the complexity of the illumination conditions and the huge differences between bearing dataset and ImageNet dataset make it impossible to use the current single model-based transfer learning for bearing defect detection. In this study, we propose a novel transitive transfer learning convolutional neural network (CNN) ensemble framework for classifying bearing surface defects. Only small-scale datasets are needed in this framework. A transfer path and transfer method selection strategy for transitive transfer learning is then proposed to train the deep learning models, which enhances the feature extraction ability of the CNN models on the basis of multiple illuminations. Ablation experiments are conducted to verify the effectiveness of the proposed method. Experimental results show that the proposed transitive transfer learning CNN ensemble framework has the accuracy rate of 97.51%. The average time for detecting each bearing is 155 ms, which can meet the requirements of industrial online detection.

Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images

Background: Melanoma is a skin cancer that starts when the melanocytes that produce the skin color pigment start to grow out of control and form a cancer. Detecting melanoma early before it spreads to the lymph nodes and other parts of the body is very important because it makes a big difference to the patient's 5-year life expectancy. Screening is the process of conducting a skin examination to suspect a mole is melanoma using dermoscopic or macroscopic images. However, manual screening takes a long time. Therefore, automatic melanoma detection is needed to speed up the melanoma detection process. The previous studies still have weakness because it has low precision or recall, which means the model cannot predict melanoma accurately. The distribution of melanoma and moles datasets is imbalanced where the number of melanomas is less than moles. In addition, in previous study, comparisons of several CNN transfer learning architectures have not been carried out on dermoscopic and macroscopic images.
 Objective: This study aims to detect melanoma using the Convolutional Neural Network (CNN) with transfer learning on dermoscopic and macroscopic melanoma images. CNN with Transfer learning is a popular method for classifying digital images with high accuracy.
 Methods: This study compares four CNN with transfer learning architectures, namely MobileNet, Xception, VGG16, and ResNet50 on dermoscopic and macroscopic image. This research also uses black-hat filtering and inpainting at the preprocessing stage to remove hair from the skin image.
 Results: MobileNet is the best model for classifying melanomas or moles in this experiment which has 83.86% of F1 score and 11 second of training time per epoch.
 Conclusion: MobileNet and Xception have high average F1 scores of 84.42% and 80.00%, so they can detect melanoma accurately even though the number of melanoma datasets is less than moles. Therefore, it can be concluded that MobileNet and Xception are suitable models for classifying melanomas and moles. However, MobileNet has the fastest training time per epoch which is 11 seconds. In the future, oversampling method can be implemented to balance the number of datasets to improve the performance of the classification model.

Gamma function based ensemble of CNN models for breast cancer detection in histopathology images

Breast cancer is the second deadliest disease amongst women worldwide. Breast histopathology image analysis is one of the most powerful ways used for the detection of tumour malignancies. Manual breast histopathology image analysis is, however, subjective, time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has become a popular and viable solution for medical image analysis due to recent advances in computer power and memory. However, the performance of the CAD models needs to be improved to use for practical purposes. Convolutional neural network (CNN) based models have achieved promising results for breast histopathological image classification. In this paper, instead of relying on a single CNN model, we have proposed a novel rank-based ensemble method by combining outcomes of three transfer learning CNN models, namely GoogleNet, VGG11 and MobileNetV3_Small. The proposed ensemble model is designed using the Gamma function for solving a 2-class classification problem of breast histopathological images. In comparison to state-of-the-art approaches, our method produces better classification results, with 99.16%, 98.24%, 98.67%, and 96.16% for 40X, 100X, 200X, and 400X levels of magnification, respectively, on a publicly accessible standard dataset called BreakHis and 96.95% on another well-known dataset called ICIAR-2018.

Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms

With the advancement in pose estimation techniques, human posture detection recently received considerable attention in many applications, including ergonomics and healthcare. When using neural network models, overfitting and poor performance are prevalent issues. Recently, convolutional neural networks (CNNs) were successfully used for human posture recognition from human images due to their superior multiscale high-level visual representations over hand-engineering low-level characteristics. However, calculating millions of parameters in a deep CNN requires a significant number of annotated examples, which prohibits many deep CNNs such as AlexNet and VGG16 from being used on issues with minimal training data. We propose a new three-phase model for decision support that integrates CNN transfer learning, image data augmentation, and hyperparameter optimization (HPO) to address this problem. The model is used as part of a new decision support framework for the optimization of hyperparameters for AlexNet, VGG16, CNN, and multilayer perceptron (MLP) models for accomplishing optimal classification results. The AlexNet and VGG16 transfer learning algorithms with HPO are used for human posture detection, while CNN and Multilayer Perceptron (MLP) were used as standard classifiers for contrast. The HPO methods are essential for machine learning and deep learning algorithms because they directly influence the behaviors of training algorithms and have a major impact on the performance of machine learning and deep learning models. We used an image data augmentation technique to increase the number of images to be used for model training to reduce model overfitting and improve classification performance using the AlexNet, VGG16, CNN, and MLP models. The optimal combination of hyperparameters was found for the four models using a random-based search strategy. The MPII human posture datasets were used to test the proposed approach. The proposed models achieved an accuracy of 91.2% using AlexNet, 90.2% using VGG16, 87.5% using CNN, and 89.9% using MLP. The study is the first HPO study executed on the MPII human pose dataset.

NSCGCN: A novel deep GCN model to diagnosis COVID-19

AimCorona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment. ObjectivesRecently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images. MethodsWe apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network). ResultsExperiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%. ConclusionOur proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN). The source code of this work will be publicly available at https://github.com/TangChaosheng/NSCGCN.

Unsound wheat kernel recognition based on deep convolutional neural network transfer learning and feature fusion

Unsound wheat kernel recognition is an important part of wheat quality inspection, and it is also a key indicator to measure wheat quality. Research on unsound wheat kernel recognition is of great significance to the correct evaluation of wheat quality. The existing researches on unsound wheat kernel recognition are mainly to directly optimize the classical classification networks, and the recognition effect is often unsatisfactory due to insufficient training data. Aiming at the problem that the recognition rate of unsound wheat kernels is not ideal due to the lack of training data, we propose a Transfer Learning Feature Fusion (TLFF) model. The model uses transfer learning and feature fusion to identify unsound wheat kernels. First, feature extraction is performed by deep Convolutional Neural Networks (CNNs) VGG-16 and VGG-19 pre-trained on the large public dataset ImageNet. Then, the features extracted by the pre-trained neural networks are fused and classified through the flattening layer, fully connected layer, Dropout layer, and Softmax layer. We conduct experiments on single model, two-model fusion, three-model fusion, and four-model fusion, and select the three-model fusion scheme to perform this task. Finally, we vote on the output results of the three best fusion models to further improve the recognition rate. The pre-trained models we use are trained on a large public dataset ImageNet. Since the scale of the dataset is very large, these pre-trained models also have good generalization performance for images other than ImageNet dataset. Therefore, although our dataset is small, we can still achieve good recognition results. Experimental results show that the recognition performance of the TLFF model is significantly better than the existing unsound wheat kernel recognition models.

Integration of deep adaptation transfer learning and online sequential extreme learning machine for cross-person and cross-position activity recognition

Deep learning (DL) has been evolving to a prevalent method in human activity recognition (HAR). However, the performance of wearable sensor based HAR models decline significantly when training data come from different persons or sensor positions, and a time-consuming data annotation is indispensible to cater for the big-data driven DL models. In this paper we proposed a fast and robust hybrid model to handle the transfer issues of wearable sensor based HAR between different persons (cross-person) and different positions (cross-position) with just a few annotated data in target domain. The model consists of three parts: (1) A convolutional neural network (CNN) with global average pooling layer to facilitate the extraction of advanced common features in source domain and target domain; (2) A domain adaptive neural network with a gradient reversal layer (DANN) and deep domain confusion network with an adaptive layer (DDC) to reduce domain shift caused by the change of persons and sensor positions; (3) An adaptive classifier based on online sequential extreme learning machine (OS-ELM) to achieve fast and accurate classification with a few annotated data in target domain. Experimental results on four public datasets verified the superiority of the proposed hybrid model over standard CNN and deep transfer learning models in adapting the classifier to new sensor locations and subjects quickly, where the HAR accuracy can be improved by at least 12% for cross-person transfer and 20% for cross-position transfer, respectively.

Enhancing ensemble diversity based on multiscale dilated convolution in image classification

Convolutional neural networks (CNNs) have achieved extraordinary success on many image classification tasks in recent years. The use of dilated convolution in a CNN can increase the network’s receptive field and improve its performance, and dilated convolution can also be used to compress a CNN to realize a lightweight model. In previous studies, multiscale dilated convolution has been adopted with a focus on improving the internal network structure of a specific CNN model. Because they enable the direct use of pretrained models, transfer learning CNNs (TL-CNNs) have been widely applied for image recognition based on small datasets. This paper proposes a novel multiscale dilated-convolution-based ensemble learning (MDCEL) method for effectively improving the performance of a pretrained CNN model. The authors' primary assumption is that semantic representations of different images can be obtained based on multiscale dilated convolution. Therefore, constructing an ensemble of diverse TL-CNN classifiers makes it possible to achieve higher performance than that offered by the traditional TL-CNN methods. The MDCEL method is highly versatile and can be applied to various conventional pretrained CNN models and lightweight CNN models. Moreover, this method does not require the modification of the internal structures of the pretrained CNN models and has high training efficiency. Experimental results on three public image classification datasets demonstrate that the proposed method outperforms the baseline traditional TL-CNN method. Compared with the baseline approach, the MDCEL approach improves the accuracy and F1 values by nearly 1–4%. In addition, an experiment on a real case dataset obtained from a manufacturing enterprise further proves the practicability of the proposed method.

Brain Tumor Detection Using Deep Learning

Abstract: One of the most leading death causes in the world is brain tumor. Tumor Detection is one of the most difficult tasks in medical image processing. In fact, the manual classification with human-assisted support can be improper prediction and diagnosis shown by medical evidence. The detection task is too difficult to perform because there is a lot of diversity in the images as brain tumors come in different shapes and textures. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance imaging (MRI). In this paper, we propose a deep learning model for the classification of brain tumors from MRI images using convolutional neural network (CNN) based on transfer learning. The implemented system explores a number of CNN architectures, image preprocessing and transfer learning model named MobilNet to achieve the better performance and accuracy. Keywords: Deep learning, convolutional neural network, Transfer learning, Brain tumor, medical image classification, MobileNet architecture, etc.

Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images.

Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayesian-CNN can overcome these limitations by regularizing automatically and by quantifying the uncertainty. We have developed a novel technique to utilize the uncertainties provided by the Bayesian-CNN that significantly improves the performance on a large fraction of the test data (about 6% improvement in accuracy on 77% of test data). Further, we provide a novel explanation for the uncertainty by projecting the data into a low dimensional space through a nonlinear dimensionality reduction technique. This dimensionality reduction enables interpretation of the test data through visualization and reveals the structure of the data in a low dimensional feature space. We show that the Bayesian-CNN can perform much better than the state-of-the-art transfer learning CNN (TL-CNN) by reducing the false negative and false positive by 11% and 7.7% respectively for the present data set. It achieves this performance with only 1.86 million parameters as compared to 134.33 million for TL-CNN. Besides, we modify the Bayesian-CNN by introducing a stochastic adaptive activation function. The modified Bayesian-CNN performs slightly better than Bayesian-CNN on all performance metrics and significantly reduces the number of false negatives and false positives (3% reduction for both). We also show that these results are statistically significant by performing McNemar's statistical significance test. This work shows the advantages of Bayesian-CNN against the state-of-the-art, explains and utilizes the uncertainties for histopathological images. It should find applications in various medical image classifications.

The application of wavelet transform of Raman spectra to facilitate transfer learning for gasoline detection and classification

This study proposed an artificial intelligence (AI) approach for rapid, automatic detection of gasoline samples from different ignitable liquid samples based on their Raman spectra. The system integrated Raman spectroscopy, Raman signal transform, and transfer learning with a convolutional neural network (CNN). A hand-held Raman spectrometer was utilized to collect the learning dataset, including 180 gasoline spectra and 170 non-gasoline spectra from 17 various types of liquid samples. Continuous wavelet transform (CWT) was adopted to transform Raman spectra into image representations to facilitate transfer learning using a pre-trained CNN for image recognition. This approach streamlined AI model training, performance verification, and assessment. Experimental results showed that the CNN model could achieve 100% classification performance in precision, sensitivity, specificity, F1 score, and accuracy. The established AI model could be adopted for the Raman spectrum collected by different accessories, such as using the point-and-shoot attachment of liquid samples in glass vials. The experimental results suggested that CWT processing of Raman scattering signals enabled effective CNN transfer learning. The study demonstrated that CWT could be applied for Raman spectra processing to develop edge AI in forensic field testing of physical evidence.

Designing a Deep Learning Hybrid Using CNN and Inception V3 Transfer Learning to Detect the Aggression Level of Deep Obsessive Compulsive Disorder in Children

The usage of Artificial intelligence in medical arena has proved to be a game changer in the detection and diagnosis of several medical conditions. In the current digital era, children with stressful medical issues are suffering from Deep Obsessive-Compulsive Disorder (DOCD). This kind of mental stress occurs in children because of the continuous usage of gadgets such as mobile phone, playing games using play stations, watching videos on tablets, etc. In most of the possibilities, single children are the ones affected with several obsessions such as stubborn activities, fighting for selfish priorities and so on. In medical terms, these kinds of complex behavioral changes are identified as DOCD. Genetic behaviors sometimes in a few group of children are also noticed as a modality difference. As symptoms are psychiatric impairment, such a child remains isolated, abnormal silence, being obsessive and repeating irrelevant words, high stress or anxiety. All medical challenges could be treated as healthcare research metrics and the gradual increase in DOCD disorder among children of this generation can be considered too. Early detection of DOCD is essential as it can help in early diagnosis but techniques to do so is unavailable currently. Deep learning-an artificial intelligence method can be utilized to detect DOCD, diagnose and treat it and bring about a positive character in children. Behavior changes in children can be classified and detected using transfer learning algorithms. In COVID-19 pandemic situation, 3% of DOCD has increased to 10-15% as a disorder. This information is retrieved from children by monitoring negative activities, unusual behavior such as nail biting, removing spectacles and placing them in the wrong place, watching tablets, mobile phones and television for more hours. Using Convolutional Neural Networks (CNN), input such as MRI (Magnetic resonance Imaging) is used for experimenting the variations in behavior with the high dimension that are analyzed from the image dataset. Using Transfer Learning with Inception V3-, CNN generalization of misophonia level can be statistically analyzed to avoid overfitting problems. By employing AI techniques, the aggression level can be predicted using data augmentation method with better accuracy and a low error rate than the existing systems. In the research it is observed that using the model employing Inception-V3 transfer learning CNN a better prediction of aggression levels can be achieved in comparison to the existing CNN model used.

Circulating Tumor Cell Identification Based on Deep Learning.

As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low cost and repeatability, CTC test has an important reference value in determining clinical results and studying the mechanism of drug resistance. However, the determination of CTC usually requires a big effort from pathologist and is also error-prone due to inexperience and fatigue. In this study, we developed a novel convolutional neural network (CNN) method to automatically detect CTCs in patients’ peripheral blood based on immunofluorescence in situ hybridization (imFISH) images. We collected the peripheral blood of 776 patients from Chifeng Municipal Hospital in China, and then used Cyttel to delete leukocytes and enrich CTCs. CTCs were identified by imFISH with CD45+, DAPI+ immunofluorescence staining and chromosome 8 centromeric probe (CEP8+). The sensitivity and specificity based on traditional CNN prediction were 95.3% and 91.7% respectively, and the sensitivity and specificity based on transfer learning were 97.2% and 94.0% respectively. The traditional CNN model and transfer learning method introduced in this paper can detect CTCs with high sensitivity, which has a certain clinical reference value for judging prognosis and diagnosing metastasis.

Automatic Classification System of Drainage Hole Blockage Based on Convolution Neural Network Transfer Learning

The blockage or failure of the drainage holes will endanger the stability of the slopes and traffic safety of a highway tunnel. This paper studies an algorithm for the automatic classification of drainage hole blockage degree based on convolutional neural network transfer learning to explore the intelligent detection method of drainage hole blockage. The model transfer method is adopted to input drainage hole image samples to retrain the pretrained network to classify new images. Experiments are performed on the collected samples of drainage hole images, and the accuracy of different network models is compared, ResNet‐18 being the best. The ResNet‐18 performance is compared using different transfer strategies and parameters. The results show that when the SGDM gradient optimisation algorithm is used and the learning rate is 0.0001, the identification effect of these samples is the best. The validation accuracy can reach 91.7%, test accuracy is 90.0%, and the effective classification of drainage hole blockage to different degrees is realised under the transfer learning strategy of ResNet‐18 model 1–34 frozen layers. Furthermore, with an expansion of the samples in the future, the identification accuracy will be further improved. The automatic classification system of the blockage degree of drainage hole greatly reduces the cost of manual detection, plays a guiding role in the maintenance of drainage pipes, and effectively improves the safety of highway tunnels and slopes.

Deep COVID 19: Deep Learning for COVID 19 Detection from X ray Images

The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy.