METHODS FOR INCREASING THE CLASSIFICATION ACCURACY BASED ON MODIFICATIONS OF THE BASIC ARCHITECTURE OF CONVOLUTIONAL NEURAL NETWORKS

Object of research: basic architectures of deep learning neural networks. Investigated problem: insufficient accuracy of solving the classification problem based on the basic architectures of deep learning neural networks. An increase in accuracy requires a significant complication of the architecture, which, in turn, leads to an increase in the required computing resources, as well as the consumption of video memory and the cost of learning/output time. Therefore, the problem arises of determining such methods for modifying basic architectures that improve the classification accuracy and require insignificant additional computing resources. Main scientific results: based on the analysis of existing methods for improving the classification accuracy on the convolutional networks of basic architectures, it is determined what is most effective: scaling the ScanNet architecture, learning the ensemble of TreeNet models, integrating several CBNet backbone networks. For computational experiments, these modifications of the basic architectures are implemented, as well as their combinations: ScanNet + TreeNet, ScanNet + CBNet. The effectiveness of these methods in comparison with basic architectures has been proven when solving the problem of recognizing malignant tumors with diagnostic images – SIIM-ISIC Melanoma Classification, the train/test set of which is presented on the Kaggle platform. The accuracy value for the area under the ROC curve metric has increased from 0.94489 (basic architecture network) to 0.96317 (network with ScanNet + CBNet modifications). At the same time, the output compared to the basic architecture (EfficientNet-b5) increased from 440 to 490 seconds, and the consumption of video memory increased from 8 to 9.2 gigabytes, which is acceptable. Innovative technological product: methods for achieving high recognition accuracy from a diagnostic signal based on deep learning neural networks of basic architectures. Scope of application of the innovative technological product: automatic diagnostics systems in the following areas: medicine, seismology, astronomy (classification by images) onboard control systems and systems for monitoring transport and vehicle flows or visitors (recognition of scenes with camera frames).

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Tobacco Leaf Grading Based on Deep Convolutional Neural Networks and Machine Vision

Journal of the ASABE ◽

10.13031/ja.14537 ◽

2021 ◽

Vol 65 (1) ◽

pp. 11-22

Author(s):

Mengyao Lu ◽

Shuwen Jiang ◽

Cong Wang ◽

Dong Chen ◽

Tian’en Chen

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Classification Model ◽

List Type ◽

Tobacco Leaves ◽

Tobacco Leaf ◽

Grading Model

HighlightsA classification model for the front and back sides of tobacco leaves was developed for application in industry.A tobacco leaf grading method that combines a CNN with double-branch integration was proposed.The A-ResNet network was proposed and compared with other classic CNN networks.The grading accuracy of eight different grades was 91.30% and the testing time was 82.180 ms, showing a relatively high classification accuracy and efficiency.Abstract. Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products. Keywords: Convolutional neural network, Deep learning, Image classification, Transfer learning, Tobacco leaf grading

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Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Sensors ◽

10.3390/s18092929 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2929 ◽

Cited By ~ 14

Author(s):

Yuanyuan Wang ◽

Chao Wang ◽

Hong Zhang

Keyword(s):

Deep Learning ◽

High Resolution ◽

Classification Accuracy ◽

Experimental Results ◽

Fine Tuning ◽

Classification Model ◽

Great Success ◽

Sar Images ◽

Convolutional Networks ◽

Ship Classification

With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.

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Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification

Entropy ◽

10.3390/e21050456 ◽

2019 ◽

Vol 21 (5) ◽

pp. 456 ◽

Cited By ~ 3

Author(s):

Hao Cheng ◽

Dongze Lian ◽

Shenghua Gao ◽

Yanlin Geng

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Model Selection ◽

Image Classification ◽

Transfer Learning ◽

Deep Neural Networks ◽

Classification Problem ◽

Distribution Model ◽

Classification Problems ◽

Information Bottleneck

Inspired by the pioneering work of the information bottleneck (IB) principle for Deep Neural Networks’ (DNNs) analysis, we thoroughly study the relationship among the model accuracy, I ( X ; T ) and I ( T ; Y ) , where I ( X ; T ) and I ( T ; Y ) are the mutual information of DNN’s output T with input X and label Y. Then, we design an information plane-based framework to evaluate the capability of DNNs (including CNNs) for image classification. Instead of each hidden layer’s output, our framework focuses on the model output T. We successfully apply our framework to many application scenarios arising in deep learning and image classification problems, such as image classification with unbalanced data distribution, model selection, and transfer learning. The experimental results verify the effectiveness of the information plane-based framework: Our framework may facilitate a quick model selection and determine the number of samples needed for each class in the unbalanced classification problem. Furthermore, the framework explains the efficiency of transfer learning in the deep learning area.

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Image Classification: A Survey

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/001.02.002 ◽

2020 ◽

Vol 1 (2) ◽

pp. 1-9

Author(s):

Ankita Singh ◽

◽

Pawan Singh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Image Classification ◽

Convolutional Neural Networks ◽

Language Processing ◽

Classification Accuracy ◽

Deep Neural Network ◽

Learning Ability ◽

Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

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A New Support Vector Machine Based on Convolution Product

Complexity ◽

10.1155/2021/9932292 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Wei-Chang Yeh ◽

Yunzhi Jiang ◽

Shi-Yi Tan ◽

Chih-Yen Yeh

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Classification Accuracy ◽

Numerical Experiments ◽

Classification Problem ◽

Support Vector ◽

Convolution Product ◽

Swarm Optimization ◽

New Information ◽

Short Time

The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has the advantages of both CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. The proposed CSVM adapts the convolution product from CNN to learn new information hidden deeply in the datasets. In addition, it uses a modified simplified swarm optimization (SSO) to help train the CSVM to update classifiers, and then the traditional SVM is implemented as the fitness for the SSO to estimate the accuracy. To evaluate the performance of the proposed CSVM, experiments were conducted to test five well-known benchmark databases for the classification problem. Numerical experiments compared favorably with those obtained using SVM, 3-layer artificial NN (ANN), and 4-layer ANN. The results of these experiments verify that the proposed CSVM with the proposed SSO can effectively increase classification accuracy.

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Structured crowdsourcing enables convolutional segmentation of histology images

Bioinformatics ◽

10.1093/bioinformatics/btz083 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3461-3467 ◽

Cited By ~ 12

Author(s):

Mohamed Amgad ◽

Habiba Elfandy ◽

Hagar Hussein ◽

Lamees A Atteya ◽

Mai A T Elsebaie ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Classification Accuracy ◽

Supplementary Information ◽

Supplementary Data ◽

Digital Slide ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Annotation Data ◽

Whole Slide Images

Abstract Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. Availability and Implementation Dataset is freely available at: https://goo.gl/cNM4EL. Supplementary information Supplementary data are available at Bioinformatics online.

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Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning

Journal of Artificial Intelligence and Soft Computing Research ◽

10.1515/jaiscr-2017-0019 ◽

2017 ◽

Vol 7 (4) ◽

pp. 265-286 ◽

Cited By ~ 41

Author(s):

Guido Bologna ◽

Yoichi Hayashi

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Predictive Accuracy ◽

Binary Classification ◽

Classification Problem ◽

Rule Extraction ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Digit Recognition ◽

Rule Complexity

AbstractRule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.

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Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

10.54216/fpa.030103 ◽

2021 ◽

pp. 43-53

Author(s):

admin admin ◽

◽

Adnan Mohsin Abdulazeez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Classification Accuracy ◽

Machine Learning Techniques ◽

Deep Convolutional Neural Networks ◽

Learning Techniques ◽

Good Classification ◽

Learning Architectures ◽

Ecg Data

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

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Detection of COVID-19 and Other Pneumonia Cases Using Convolutional Neural Networks and X-ray Images

Ingeniería e Investigación ◽

10.15446/ing.investig.v42n1.90289 ◽

2021 ◽

Vol 42 (1) ◽

pp. e90289

Author(s):

Carlos Eduardo Belman López

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Network Capacity ◽

Superior Performance ◽

Data Set ◽

X Ray ◽

Regularization Techniques ◽

The Impact

Given that it is fundamental to detect positive COVID-19 cases and treat affected patients quickly to mitigate the impact of the virus, X-ray images have been subjected to research regarding COVID-19, together with deep learning models, eliminating disadvantages such as the scarcity of RT-PCR test kits, their elevated costs, and the long wait for results. The contribution of this paper is to present new models for detecting COVID-19 and other cases of pneumonia using chest X-ray images and convolutional neural networks, thus providing accurate diagnostics in binary and 4-classes classification scenarios. Classification accuracy was improved, and overfitting was prevented by following 2 actions: (1) increasing the data set size while the classification scenarios were balanced; and (2) adding regularization techniques and performing hyperparameter optimization. Additionally, the network capacity and size in the models were reduced as much as possible, making the final models a perfect option to be deployed locally on devices with limited capacities and without the need for Internet access. The impact of key hyperparameters was tested using modern deep learning packages. The final models obtained a classification accuracy of 99,17 and 94,03% for the binary and categorical scenarios, respectively, achieving superior performance compared to other studies in the literature, and requiring a significantly lower number of parameters. The models can also be placed on a digital platform to provide instantaneous diagnostics and surpass the shortage of experts and radiologists.

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