Classification of Indonesian Batik Using Deep Learning Techniques and Data Augmentation

Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis. Hence, we propose a deep learning-based application for effective classification of five types of psoriasis namely, plaque, guttate, inverse, pustular, and erythrodermic as well as the prediction of normal skin. We used 172 images of normal skin from the BFL NTU dataset and 301 images of psoriasis from the Dermnet dataset. The input sample images underwent image preprocessing including data augmentation, enhancement, and segmentation which was followed by color, texture, and shape feature extraction. Two deep learning algorithms of convolutional neural network (CNN) and long short-term memory (LSTM) were applied with the classification models being trained with 80% of the images. The reported accuracies of CNN and LSTM are 84.2% and 72.3%, respectively. A paired sample T-test exhibited significant differences between the accuracies generated by the two deep learning algorithms with a p < 0.001 . The accuracies reported from this study demonstrate potential of this deep learning application to be applied to other areas of dermatology for better prediction.

Download Full-text

Classification of Ecological Data by Deep Learning

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420520102 ◽

2020 ◽

Vol 34 (13) ◽

pp. 2052010

Author(s):

Shaobo Liu ◽

Frank Y. Shih ◽

Gareth Russell ◽

Kimberly Russell ◽

Hai Phan

Keyword(s):

Deep Learning ◽

Computational Models ◽

Data Augmentation ◽

Small Data ◽

Learning Models ◽

Ecological Data ◽

Accuracy Rate ◽

Learning Techniques ◽

Residual Block

Ecologists have been studying different computational models in the classification of ecological species. In this paper, we intend to take advantages of variant deep-learning models, including LeNet, AlexNet, VGG models, residual neural network, and inception models, to classify ecological datasets, such as bee wing and butterfly. Since the datasets contain relatively small data samples and unbalanced samples in each class, we apply data augmentation and transfer learning techniques. Furthermore, newly designed inception residual and inception modules are developed to enhance feature extraction and increase classification rates. As comparing against currently available deep-learning models, experimental results show that the proposed inception residual block can avoid the vanishing gradient problem and achieve a high accuracy rate of 92%.

Download Full-text

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Neural Computing and Applications ◽

10.1007/s00521-021-06352-5 ◽

2021 ◽

Author(s):

Hamdi Altaheri ◽

Ghulam Muhammad ◽

Mansour Alsulaiman ◽

Syed Umar Amin ◽

Ghadir Ali Altuwaijri ◽

...

Keyword(s):

Deep Learning ◽

Motor Imagery ◽

Learning Techniques ◽

Electroencephalogram Eeg

Download Full-text

An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽

10.3390/info12060249 ◽

2021 ◽

Vol 12 (6) ◽

pp. 249

Author(s):

Xin Jin ◽

Yuanwen Zou ◽

Zhongbing Huang

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Image Classification ◽

Classification Accuracy ◽

Data Augmentation ◽

Cycle Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

Download Full-text

Deep Learning for Laryngopharyngeal Reflux Diagnosis

Applied Sciences ◽

10.3390/app11114753 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4753

Author(s):

Gen Ye ◽

Chen Du ◽

Tong Lin ◽

Yan Yan ◽

Jack Jiang

Keyword(s):

Deep Learning ◽

Speech Processing ◽

Data Augmentation ◽

Laryngopharyngeal Reflux ◽

Ph Monitoring ◽

Binary Classification ◽

Classification Problem ◽

Learning Approaches ◽

Learning Techniques ◽

Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

Download Full-text

Classification of Dermoscopy Skin Images with the Application of Deep Learning Techniques

Proceedings of the 5th Brazilian Technology Symposium - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-030-57566-3_7 ◽

2020 ◽

pp. 73-81

Author(s):

Pablo David Minango Negrete ◽

Yuzo Iano ◽

Ana Carolina Borges Monteiro ◽

Reinaldo Padilha França ◽

Gabriel Gomes de Oliveira ◽

...

Keyword(s):

Deep Learning ◽

Learning Techniques

Download Full-text

Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4283.099320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 87-93

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Literature Review ◽

Congenital Cataract ◽

Low Cost ◽

Current Situation ◽

Learning Techniques ◽

Research Questions ◽

New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

Download Full-text

Stock Pattern Classification from Charts using Deep Learning Algorithms

Academic Perspective Procedia ◽

10.33793/acperpro.03.01.89 ◽

2020 ◽

Vol 3 (1) ◽

pp. 445-454

Author(s):

Celal Buğra Kaya ◽

Alperen Yılmaz ◽

Gizem Nur Uzun ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Pattern Classification ◽

Short Term Memory ◽

Stock Exchange ◽

Learning Techniques ◽

Proposed Model ◽

Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

Download Full-text