Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

10.20944/preprints202003.0204.v1 ◽

2020 ◽

Author(s):

Aliyu Abubakar ◽

Mohammed Ajuji ◽

Ibrahim Usman Yahya

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

Standard Technique ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Models ◽

Learning Techniques

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Journal of the Nigerian Society of Physical Sciences ◽

10.46481/jnsps.2021.308 ◽

2021 ◽

pp. 385-394

Author(s):

V Umarani ◽

A Julian ◽

J Deepa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Process ◽

Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

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A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8468 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1925-1930

Author(s):

Ambeshwar Kumar ◽

R. Manikandan ◽

Robbi Rahim

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Brain Tumor ◽

Tumor Detection ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Convolution Algorithm ◽

The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models

Remote Sensing ◽

10.3390/rs12101581 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1581 ◽

Cited By ~ 2

Author(s):

Daniel Perez ◽

Kazi Islam ◽

Victoria Hill ◽

Richard Zimmerman ◽

Blake Schaeffer ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transfer Learning ◽

Satellite Images ◽

Support Vector ◽

Learning Approach ◽

Learning Models ◽

Learning Techniques ◽

The World ◽

New Locations

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.

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Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

Sustainability ◽

10.3390/su132212653 ◽

2021 ◽

Vol 13 (22) ◽

pp. 12653

Author(s):

Shahzad Aslam ◽

Nasir Ayub ◽

Umer Farooq ◽

Muhammad Junaid Alvi ◽

Fahad R. Albogamy ◽

...

Keyword(s):

Machine Learning ◽

Smart Grids ◽

Load Forecasting ◽

Error Rates ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Support Vector ◽

Optimization Approach ◽

Daily Consumption ◽

Learning Techniques

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

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Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19

Computational Intelligence and Neuroscience ◽

10.1155/2021/9996737 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Talal S. Qaid ◽

Hussein Mazaar ◽

Mohammad Yahya H. Al-Shamri ◽

Mohammed S. Alqahtani ◽

Abeer A. Raweh ◽

...

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

Healthcare Systems ◽

Viral Pneumonia ◽

Machine Learning Techniques ◽

Full Potential ◽

Learning Models ◽

Local Data ◽

Data Set ◽

Learning Techniques

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.

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Machine learning techniques as an eﬃcient alternative diagnostic tool for COVID-19 cases

10.22514/sv.2021.110 ◽

2021 ◽

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Predictive Power ◽

Characteristic Curve ◽

False Negative ◽

Machine Learning Techniques ◽

Support Vector ◽

X Ray ◽

Learning Techniques

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

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TISSUE CLASSIFICATION FOR COLORECTAL CANCER UTILIZING TECHNIQUES OF DEEP LEARNING AND MACHINE LEARNING

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237221500228 ◽

2021 ◽

pp. 2150022

Author(s):

Kasikrit Damkliang ◽

Thakerng Wongsirichot ◽

Paramee Thongsuksai

Keyword(s):

Colorectal Cancer ◽

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Texture Classification ◽

False Positive Rate ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Cancer Tissues

Since the introduction of image pattern recognition and computer vision processing, the classification of cancer tissues has been a challenge at pixel-level, slide-level, and patient-level. Conventional machine learning techniques have given way to Deep Learning (DL), a contemporary, state-of-the-art approach to texture classification and localization of cancer tissues. Colorectal Cancer (CRC) is the third ranked cause of death from cancer worldwide. This paper proposes image-level texture classification of a CRC dataset by deep convolutional neural networks (CNN). Simple DL techniques consisting of transfer learning and fine-tuning were exploited. VGG-16, a Keras pre-trained model with initial weights by ImageNet, was applied. The transfer learning architecture and methods responding to VGG-16 are proposed. The training, validation, and testing sets included 5000 images of 150 × 150 pixels. The application set for detection and localization contained 10 large original images of 5000 × 5000 pixels. The model achieved F1-score and accuracy of 0.96 and 0.99, respectively, and produced a false positive rate of 0.01. AUC-based evaluation was also measured. The model classified ten large previously unseen images from the application set represented in false color maps. The reported results show the satisfactory performance of the model. The simplicity of the architecture, configuration, and implementation also contributes to the outcome this work.

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Deep Learning and Machine Learning Techniques to Classify Electrical and Electronic Equipment

10.1115/detc2021-71403 ◽

2021 ◽

Author(s):

Shuaizhou Hu ◽

Xinyao Zhang ◽

Hao-yu Liao ◽

Xiao Liang ◽

Minghui Zheng ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Electronic Waste ◽

Consumer Electronics ◽

Machine Learning Techniques ◽

Support Vector ◽

Model Parameters ◽

Scale Invariant ◽

Learning Techniques ◽

Scale Invariant Feature

Abstract Remanufacturing sites often receive products with different brands, models, conditions, and quality levels. Proper sorting and classification of the waste stream is a primary step in efficiently recovering and handling used products. The correct classification is particularly crucial in future electronic waste (e-waste) management sites equipped with Artificial Intelligence (AI) and robotic technologies. Robots should be enabled with proper algorithms to recognize and classify products with different features and prepare them for assembly and disassembly tasks. In this study, two categories of Machine Learning (ML) and Deep Learning (DL) techniques are used to classify consumer electronics. ML models include Naïve Bayes with Bernoulli, Gaussian, Multinomial distributions, and Support Vector Machine (SVM) algorithms with four kernels of Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid. While DL models include VGG-16, GoogLeNet, Inception-v3, Inception-v4, and ResNet-50. The above-mentioned models are used to classify three laptop brands, including Apple, HP, and ThinkPad. First the Edge Histogram Descriptor (EHD) and Scale Invariant Feature Transform (SIFT) are used to extract features as inputs to ML models for classification. DL models use laptop images without pre-processing on feature extraction. The trained models are slightly overfitting due to the limited dataset and complexity of model parameters. Despite slight overfitting, the models can identify each brand. The findings prove that DL models outperform them of ML. Among DL models, GoogLeNet has the highest performance in identifying the laptop brands.

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