scholarly journals Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yao Yao ◽  
Shuiping Gou ◽  
Ru Tian ◽  
Xiangrong Zhang ◽  
Shuixiang He
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Network Performance ◽  
Early Stage ◽  
Classification Performance ◽  
Disease Classification ◽  
Colorectal Disease ◽  
Automated Classification ◽  
Initial Network ◽  
Colorectal Diseases

Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classification and segmentation. Because of label unbalanced and difficult colorectal data, the classification based on self-paced transfer VGG network (STVGG) is proposed. ImageNet pretraining network parameters are transferred to VGG network with training colorectal data to acquire good initial network performance. And self-paced learning is used to optimize the network so that the classification performance of label unbalanced and difficult samples is improved. In order to assist the colonoscopist to accurately determine whether the polyp needs surgical resection, feature of trained STVGG model is shared to Unet segmentation network as the encoder part and to avoid repeat learning of polyp segmentation model. The experimental results on 3061 colorectal images illustrated that the proposed method obtained higher classification accuracy (96%) and segmentation performance compared with a few other methods. The polyp can be segmented accurately from around tissues by the proposed method. The segmentation results underpin the potential of deep learning methods for assisting colonoscopist in identifying polyps and enabling timely resection of these polyps at an early stage.

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

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.

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

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.

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

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 291 ◽  
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.

scholarly journals Dissecting Deep Learning Networks—Visualizing Mutual Information

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 823 ◽  
Author(s):  
Hui Fang ◽  
Victoria Wang ◽  
Motonori Yamaguchi
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Network Performance ◽  
Classification Performance ◽  
Knowledge Gap ◽  
Accuracy Evaluation ◽  
Learning Networks ◽  
Theoretical Understanding ◽  
Effective Network ◽  
Visualization Techniques

Deep Learning (DL) networks are recent revolutionary developments in artificial intelligence research. Typical networks are stacked by groups of layers that are further composed of many convolutional kernels or neurons. In network design, many hyper-parameters need to be defined heuristically before training in order to achieve high cross-validation accuracies. However, accuracy evaluation from the output layer alone is not sufficient to specify the roles of the hidden units in associated networks. This results in a significant knowledge gap between DL’s wider applications and its limited theoretical understanding. To narrow the knowledge gap, our study explores visualization techniques to illustrate the mutual information (MI) in DL networks. The MI is a theoretical measurement, reflecting the relationship between two sets of random variables even if their relationship is highly non-linear and hidden in high-dimensional data. Our study aims to understand the roles of DL units in classification performance of the networks. Via a series of experiments using several popular DL networks, it shows that the visualization of MI and its change patterns between the input/output with the hidden layers and basic units can facilitate a better understanding of these DL units’ roles. Our investigation on network convergence suggests a more objective manner to potentially evaluate DL networks. Furthermore, the visualization provides a useful tool to gain insights into the network performance, and thus to potentially facilitate the design of better network architectures by identifying redundancy and less-effective network units.

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

2019 ◽  
Vol 9 (1) ◽  
pp. 128 ◽  
Author(s):  
Yoshihide Sawada ◽  
Yoshikuni Sato ◽  
Toru Nakada ◽  
Shunta Yamaguchi ◽  
Kei Ujimoto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Estimation Method ◽  
Classification Performance ◽  
Point Of View ◽  
Disease Classification ◽  
Training Data ◽  
Target Domain ◽  
Target Vector ◽  
Repulsive Forces ◽  
Output Space

This paper proposes a target vector modification method for the all-transfer deep learning (ATDL) method. Deep neural networks (DNNs) have been used widely in many applications; however, the DNN has been known to be problematic when large amounts of training data are not available. Transfer learning can provide a solution to this problem. Previous methods regularize all layers, including the output layer, by estimating the relation vectors, which are then used instead of one-hot target vectors of the target domain. These vectors are estimated by averaging the target domain data of each target domain label in the output space. This method improves the classification performance, but it does not consider the relation between the relation vectors. From this point of view, we propose a relation vector modification based on constrained pairwise repulsive forces. High pairwise repulsive forces provide large distances between the relation vectors. In addition, the risk of divergence is mitigated by the constraint based on distributions of the output vectors of the target domain data. We apply our method to two simulation experiments and a disease classification using two-dimensional electrophoresis images. The experimental results show that reusing all layers through our estimation method is effective, especially for a significantly small number of the target domain data.

scholarly journals DEEP MULTI-TASK LEARNING FOR TREE GENERA CLASSIFICATION

2018 ◽  
Vol IV-2 ◽  
pp. 153-159 ◽  
Author(s):  
C. Ko ◽  
J. Kang ◽  
G. Sohn
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Network Performance ◽  
Training Sample ◽  
Training Data ◽  
Training Dataset ◽  
Main Task ◽  
Data Generation ◽  
Multiple View

The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) &amp;ndash; Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (<i>L</i><sub>cd</sub>) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7&amp;thinsp;% to 91.0&amp;thinsp;% (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

scholarly journals Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 131
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Disease Severity ◽  
Early Stage ◽  
Decision Rules ◽  
Plant Diseases ◽  
Disease Classification ◽  
Rust Disease ◽  
Novel Approach ◽  
The Common

Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
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.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

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