Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

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DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w12-81-2019 ◽

2019 ◽

Vol XLII-2/W12 ◽

pp. 81-85 ◽

Cited By ~ 1

Author(s):

D. A. Gavrilov ◽

N. N. Shchelkunov ◽

A. V. Melerzanov

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Intelligent Systems ◽

State Of The Art ◽

Skin Lesions ◽

Deep Convolutional Neural Networks ◽

Melanoma Diagnosis ◽

Malignant Skin ◽

Diagnosis Accuracy ◽

Visual Diagnosis

<p><strong>Abstract.</strong> Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.</p>

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Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

Electronics ◽

10.3390/electronics10091079 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1079

Author(s):

Abhishek Varshney ◽

Samit Kumar Ghosh ◽

Sibasankar Padhy ◽

Rajesh Kumar Tripathy ◽

U. Rajendra Acharya

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Classification Accuracy ◽

Mental Arithmetic ◽

Cognitive Workload ◽

Automated Classification ◽

Eeg Signals ◽

Arithmetic Task ◽

Mental Arithmetic Task

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

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Voice Pathology Detection and Classification Using Convolutional Neural Network Model

Applied Sciences ◽

10.3390/app10113723 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3723 ◽

Cited By ~ 1

Author(s):

Mazin Abed Mohammed ◽

Karrar Hameed Abdulkareem ◽

Salama A. Mostafa ◽

Mohd Khanapi Abd Ghani ◽

Mashael S. Maashi ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Early Stage ◽

Speech Pathology ◽

Appropriate Treatment ◽

Pathology Classification ◽

Wide Range ◽

Voice Pathology Detection ◽

Voice Pathologies

Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.

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Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Computers ◽

10.3390/computers9020029 ◽

2020 ◽

Vol 9 (2) ◽

pp. 29 ◽

Cited By ~ 4

Author(s):

Mohamed Loey ◽

Mukdad Naman ◽

Hala Zayed

Keyword(s):

Neural Network ◽

Transfer Learning ◽

Blood Cells ◽

Classification Accuracy ◽

Automated Classification ◽

Classification Models ◽

Fatal Disease ◽

Microscopic Images ◽

Traditional Approaches ◽

Better Than

Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.

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Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/2128/1/012013 ◽

2021 ◽

Vol 2128 (1) ◽

pp. 012013

Author(s):

Laila Moataz ◽

Gouda I. Salama ◽

Mohamed H. Abd Elazeem

Keyword(s):

Neural Network ◽

Skin Cancer ◽

Classification Accuracy ◽

Skin Lesions ◽

Fine Tuning ◽

Deep Convolutional Neural Networks ◽

Proposed Model ◽

Prior Models ◽

Cancer Diseases

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

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Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16101688 ◽

2019 ◽

Vol 16 (10) ◽

pp. 1688 ◽

Cited By ~ 1

Author(s):

Zhichao Li ◽

Jilin Huang ◽

Zhiping Hu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Speech Disorder ◽

Diagnostic Methods ◽

Common Disease ◽

Long Duration ◽

Wide Range ◽

Rapid Diagnostic Method ◽

Voice Data

Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and avoid misdiagnosis, the search for an affordable and rapid diagnostic method is becoming more and more important for chronic pharyngitis research. Speech disorder is one of the typical symptoms of patients with chronic pharyngitis. This paper introduces a convolutional neural network model for diagnosis based on the typical symptom of speech disorder. First of all, the voice data is converted into a speech spectrogram, which can better output the speech characteristic information and lay a foundation for computer diagnosis and discrimination. Second, we construct a deep convolutional neural network for the diagnosis of chronic pharyngitis through the design of the structure, the design of the network layer, and the description of the function. Finally, we perform a parameter optimization experiment on the convolutional neural network and judge the recognition efficiency of chronic pharyngitis. The results show that the convolutional neural network has a high recognition rate for patients with chronic pharyngitis and has a good diagnostic effect.

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Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/1683013 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Kai Zhang ◽

Guanghua Xu ◽

Longtin Chen ◽

Peiyuan Tian ◽

ChengCheng Han ◽

...

Keyword(s):

Neural Network ◽

Transfer Learning ◽

Motor Imagery ◽

Classification Accuracy ◽

Classification Model ◽

Deep Convolutional Neural Networks ◽

Contrast Model ◽

Expected Performance ◽

Kappa Value ◽

Training Strategies

In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brain. This issue may lead to variations in feature distribution of electroencephalogram (EEG) across subjects, which greatly reduces the generalization ability of a classifier. Although subject-dependent (SD) strategy provides a promising way to solve the problem of personalized classification, it cannot achieve expected performance due to the limitation of the amount of data especially for a deep neural network (DNN) classification model. Herein, we propose an instance transfer subject-independent (ITSD) framework combined with a convolutional neural network (CNN) to improve the classification accuracy of the model during motor imagery (MI) task. The proposed framework consists of the following steps. Firstly, an instance transfer learning based on the perceptive Hash algorithm is proposed to measure similarity of spectrogram EEG signals between different subjects. Then, we develop a CNN to decode these signals after instance transfer learning. Next, the performance of classifications by different training strategies (subject-independent- (SI-) CNN, SD-CNN, and ITSD-CNN) are compared. To verify the effectiveness of the algorithm, we evaluate it on the dataset of BCI competition IV-2b. Experiments show that the instance transfer learning can achieve positive instance transfer using a CNN classification model. Among the three different training strategies, the average classification accuracy of ITSD-CNN can achieve 94.7±2.6 and obtain obvious improvement compared with a contrast model p<0.01. Compared with other methods proposed in previous research, the framework of ITSD-CNN outperforms the state-of-the-art classification methods with a mean kappa value of 0.664.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

Procedia Computer Science ◽

10.1016/j.procs.2018.11.111 ◽

2018 ◽

Vol 145 ◽

pp. 488-494 ◽

Cited By ~ 3

Author(s):

Aleksandr Sboev ◽

Alexey Serenko ◽

Roman Rybka ◽

Danila Vlasov ◽

Andrey Filchenkov

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Classification Accuracy ◽

Spiking Neural Network ◽

Network Parameters

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Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

SN Applied Sciences ◽

10.1007/s42452-020-03974-7 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Charles Gbenga Williams ◽

Oluwapelumi O. Ojuri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hydraulic Conductivity ◽

Linear Regression ◽

Multiple Linear Regression ◽

Coarse Grained ◽

Soil Types ◽

Wide Range ◽

Artificial Neural ◽

Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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