Improvement in Classification Performance Based on Target Vector Modification for All-Transfer Deep Learning

Yoshihide Sawada; Yoshikuni Sato; Toru Nakada; Shunta Yamaguchi; Kei Ujimoto; Nobuhiro Hayashi

doi:10.3390/app9010128

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Healthcare ◽

10.3390/healthcare8030291 ◽

2020 ◽

Vol 8 (3) ◽

pp. 291 ◽

Cited By ~ 1

Author(s):

Chunwu Yin ◽

Zhanbo Chen

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Style ◽

Classification Performance ◽

Disease Classification ◽

Training Data ◽

Training Procedure ◽

Training Approach ◽

Classification Of Diseases ◽

Deep Forest

Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process. The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance. Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled samples to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples. The experimental results show that our proposed model can achieve good performance in disease classification and disease-causing gene identification.

AOHDL: Archimedes Optimization Based Hybrid Deep Learning Model for Soybean Plant Disease Classification

10.21203/rs.3.rs-302084/v1 ◽

2021 ◽

Author(s):

J. Annrose ◽

N. Herald Anantha Rufus ◽

C. R. Edwin Selva Rex ◽

D. Godwin Immanuel

Keyword(s):

Deep Learning ◽

Optimization Algorithm ◽

Short Term Memory ◽

Learning Model ◽

Disease Classification ◽

Training Data ◽

Machine Learning Techniques ◽

Soybean Plant ◽

Lower Accuracy ◽

Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

10.21203/rs.3.rs-281525/v1 ◽

2021 ◽

Author(s):

J. Annrose ◽

N. Herald Anantha Rufus ◽

C. R. Edwin Selva Rex ◽

D. Godwin Immanuel

Keyword(s):

Deep Learning ◽

Optimization Algorithm ◽

Short Term Memory ◽

Learning Model ◽

Disease Classification ◽

Training Data ◽

Machine Learning Techniques ◽

Soybean Plant ◽

Lower Accuracy ◽

Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

Alinteri Journal of Agricultural Sciences ◽

10.47059/alinteri/v36i1/ajas21065 ◽

2021 ◽

Vol 36 (1) ◽

pp. 443-450

Author(s):

Mounika Jammula

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Plant Disease ◽

Bilateral Filter ◽

Noise Removal ◽

Classification Performance ◽

Disease Classification ◽

Gaussian Kernel ◽

Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

Neural Computing and Applications ◽

10.1007/s00521-021-06044-0 ◽

2021 ◽

Author(s):

Fouzia Altaf ◽

Syed M. S. Islam ◽

Naeem Khalid Janjua

Keyword(s):

Deep Learning ◽

Medical Imaging ◽

Transfer Learning ◽

Medical Images ◽

Training Data ◽

Target Domain ◽

Data Set ◽

Learning Technique ◽

Multi Class Classification ◽

Effective Transfer

AbstractDeep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification.

IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET

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

10.5194/isprs-archives-xliii-b3-2021-369-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 369-376

Author(s):

H. Yassine ◽

K. Tout ◽

M. Jaber

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Satellite Imagery ◽

Vegetation Index ◽

Near Infrared ◽

Spectral Feature ◽

Classification Performance ◽

Training Data ◽

Data Set ◽

Sentinel 2

Abstract. Machine learning (ML) has proven useful for a very large number of applications in several domains. It has realized a remarkable growth in remote-sensing image analysis over the past few years. Deep Learning (DL) a subset of machine learning were applied in this work to achieve a better classification of Land Use Land Cover (LULC) in satellite imagery using Convolutional Neural Networks (CNNs). EuroSAT benchmarking data set is used as training data set which uses Sentinel-2 satellite images. Sentinel-2 provides images with 13 spectral feature bands, but surprisingly little attention has been paid to these features in deep learning models. The majority of applications focused only on using RGB due to high availability of the RGB models in computer vision. While RGB gives an accuracy of 96.83% using CNN, we are presenting two approaches to improve the classification performance of Sentinel-2 images. In the first approach, features are extracted from 13 spectral feature bands of Sentinel-2 instead of RGB which leads to accuracy of 98.78%. In the second approach features are extracted from 13 spectral bands of Sentinel-2 in addition to calculated indices used in LULC like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR), etc. which gives a better accuracy of 99.58%.

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

Elektronika ir Elektrotechnika ◽

10.5755/j02.eie.27642 ◽

2021 ◽

Vol 27 (1) ◽

pp. 48-59

Author(s):

Thanh-Nghia Nguyen ◽

Thanh-Hai Nguyen

Keyword(s):

Heart Disease ◽

Deep Learning ◽

Classification Accuracy ◽

Classification Performance ◽

Disease Classification ◽

Kernel Size ◽

S Waves ◽

Learning Framework ◽

Good Classification Performance ◽

Fully Connected

Heart disease classification with high accuracy can support the physician’s correct decision on patients. This paper proposes a kernel size calculation based on P, Q, R, and S waves of one heartbeat to enhance classification accuracy in a deep learning framework. In addition, Electrocardiogram (ECG) signals were filtered using wavelet transform with dmey wavelet, in which the shape of the dmey is closed to that of one heartbeat. With this selected dmey, each heartbeat was standardized with 300 samples for calculation of kernel sizes so that it contains most features in each heartbeat. Therefore, in this research, with 103,459 heart rhythms from the MIT-BIH Arrhythmia Database, the proposed approach for calculation of kernel sizes is effective with seven convolutional layers and other fully connected layers in a Deep Neural Network (DNN). In particular, with five types of heart disease, the result of the high classification accuracy is about 99.4 %. It means that the proposed kernel size calculation in the convolutional layers can achieve good classification performance and it may be developed for classifying different types of disease.

Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.645952 ◽

2021 ◽

Vol 15 ◽

Author(s):

Yu Pei ◽

Zhiguo Luo ◽

Ye Yan ◽

Huijiong Yan ◽

Jing Jiang ◽

...

Keyword(s):

Deep Learning ◽

Motor Imagery ◽

Data Augmentation ◽

Brain Area ◽

Comparative Investigation ◽

Classification Performance ◽

Training Data ◽

Learning Technology ◽

Data Generation ◽

Public Datasets

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.

Differentially Private Correlation Alignment for Domain Adaptation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/502 ◽

2021 ◽

Author(s):

Kaizhong Jin ◽

Xiang Cheng ◽

Jiaxi Yang ◽

Kaiyuan Shen

Keyword(s):

Covariance Matrix ◽

Domain Adaptation ◽

Estimation Method ◽

Training Data ◽

Target Domain ◽

Source Domain ◽

Feature Spaces ◽

Benchmark Datasets ◽

Low Dimensional ◽

Target Data

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. As a simple and efficient method for domain adaptation, correlation alignment transforms the distribution of the source domain by utilizing the covariance matrix of the target domain, such that a model trained on the transformed source data can be applied to the target data. However, when source and target domains come from different institutes, exchanging information between the two domains might pose a potential privacy risk. In this paper, for the first time, we propose a differentially private correlation alignment approach for domain adaptation called PRIMA, which can provide privacy guarantees for both the source and target data. In PRIMA, to relieve the performance degradation caused by perturbing the covariance matrix in high dimensional setting, we present a random subspace ensemble based covariance estimation method which splits the feature spaces of source and target data into several low dimensional subspaces. Moreover, since perturbing the covariance matrix may destroy its positive semi-definiteness, we develop a shrinking based method for the recovery of positive semi-definiteness of the covariance matrix. Experimental results on standard benchmark datasets confirm the effectiveness of our approach.

Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents

Information ◽

10.3390/info10080248 ◽

2019 ◽

Vol 10 (8) ◽

pp. 248 ◽

Cited By ~ 3

Author(s):

Sumam Francis ◽

Jordy Van Landeghem ◽

Marie-Francine Moens

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

State Of The Art ◽

Named Entity Recognition ◽

Training Data ◽

Entity Recognition ◽

Language Models ◽

Reasonable Assumption ◽

Target Domain ◽

Named Entity

Recent deep learning approaches have shown promising results for named entity recognition (NER). A reasonable assumption for training robust deep learning models is that a sufficient amount of high-quality annotated training data is available. However, in many real-world scenarios, labeled training data is scarcely present. In this paper we consider two use cases: generic entity extraction from financial and from biomedical documents. First, we have developed a character based model for NER in financial documents and a word and character based model with attention for NER in biomedical documents. Further, we have analyzed how transfer learning addresses the problem of limited training data in a target domain. We demonstrate through experiments that NER models trained on labeled data from a source domain can be used as base models and then be fine-tuned with few labeled data for recognition of different named entity classes in a target domain. We also witness an interest in language models to improve NER as a way of coping with limited labeled data. The current most successful language model is BERT. Because of its success in state-of-the-art models we integrate representations based on BERT in our biomedical NER model along with word and character information. The results are compared with a state-of-the-art model applied on a benchmarking biomedical corpus.