scholarly journals Automatic classification of cervical cancer from cytological images by using convolutional neural network

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Miao Wu ◽  
Chuanbo Yan ◽  
Huiqiang Liu ◽  
Qian Liu ◽  
Yi Yin
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Medical Condition ◽  
Classification Model ◽  
Original Image ◽  
Paired Samples ◽  
Incidence And Mortality ◽  
Significant Difference

Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists’ diagnostic accuracy of CC in such regions, we tried to propose an intelligent and efficient classification model for CC based on convolutional neural network (CNN) with relatively simple architecture compared with others. The model was trained and tested by two groups of image datasets, respectively, which were original image group with a volume of 3012 datasets and augmented image group with a volume of 108432 datasets. Each group has a number of fixed-size RGB images (227*227) of keratinizing squamous, non-keratinizing squamous, and basaloid squamous. The method of three-folder cross-validation was applied to the model. And the classification accuracy of the models, overall, 93.33% for original image group and 89.48% for augmented image group. The improvement of 3.85% has been achieved by using augmented images as input data for the model. The results got from paired-samples ttest indicated that two models’ classification accuracy has a significant difference (P<0.05). The developed scheme we proposed was useful for classifying CCs from cytological images and the model can be served as a pathologist assistance to improve the doctor’s diagnostic level of CC, which has a great meaning and huge potential application in poor medical condition areas in China.

scholarly journals A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jianghui Wen ◽  
Yeshu Liu ◽  
Yu Shi ◽  
Haoran Huang ◽  
Bing Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Messenger Rna ◽  
Biological Activities ◽  
Classification Model ◽  
Support Vector ◽  
Non Coding Rna ◽  
Recognition Ability ◽  
The Difference

Abstract Background Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA. Results First, the difference in the k-mer frequency distribution between lncRNA and mRNA sequences is considered in this paper, and they are transformed into the k-mer frequency matrix. Moreover, k-mers with more species are screened by relative entropy. The classification model of the lncRNA and mRNA sequences is then proposed by inputting the k-mer frequency matrix and training the convolutional neural network. Finally, the optimal k-mer combination of the classification model is determined and compared with other machine learning methods in humans, mice and chickens. The results indicate that the proposed model has the highest classification accuracy. Furthermore, the recognition ability of this model is verified to a single sequence. Conclusion We established a classification model for lncRNA and mRNA based on k-mers and the convolutional neural network. The classification accuracy of the model with 1-mers, 2-mers and 3-mers was the highest, with an accuracy of 0.9872 in humans, 0.8797 in mice and 0.9963 in chickens, which is better than those of the random forest, logistic regression, decision tree and support vector machine.

scholarly journals Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ankita Tyagi ◽  
Ritika Mehra
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Heart Diseases ◽  
Classification Model ◽  
Ecg Signal ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Selection Of

AbstractAutomatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning

2020 ◽  
Vol 36 (5) ◽  
pp. 743-749
Author(s):  
Xingwang Li ◽  
Xiaofei Fan ◽  
Lili Zhao ◽  
Sheng Huang ◽  
Yi He ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Multispectral Imaging ◽  
Classification Model ◽  
One Dimensional ◽  
Pepper Seed ◽  
Better Than

HighlightsThis study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN).Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM.In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement.Abstract. When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification.

scholarly journals A Sports Training Video Classification Model Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yunjun Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Camera Calibration ◽  
Classification Accuracy ◽  
Classification Model ◽  
Video Classification ◽  
Sports Training ◽  
Training Video ◽  
Model Based

A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.

scholarly journals An Improved Convolutional Neural Network for Text Classification

2021 ◽  
Vol 2066 (1) ◽  
pp. 012091
Author(s):  
Xiaojing Fan ◽  
A Runa ◽  
Zhili Pei ◽  
Mingyang Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Classification Model ◽  
Classification Problems ◽  
Training Time ◽  
Accuracy And Precision ◽  
And Training

Abstract This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN, SDCNN further improves the classification performance of the model, and its classification accuracy and precision can reach 98.96% and 85.61%, respectively, indicating that SDCNN has more advantages in text classification problems.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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