• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.97-99
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• 2021

As the aging society continues in recent years, interest in dementia is increasing. Among them, Alzheimer's disease is a degenerative brain disease that accounts for the largest percentage of all dementia patients, with the medical community currently not offering clear prevention and treatment for Alzheimer's disease, and the importance of early treatment and early prevention is emphasized. In this paper, we intend to find the most efficient activation function by combining various activation functions centering on convolutional neural networks using MRI datasets of normal people and patients with Alzheimer's disease. In addition, it is intended to be used as a dementia classification modeling suitable for the medical field in the future through Alzheimer's dementia classification modeling.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.354-360
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• 2022

Metabolic disease is a disease with a prevalence of 26% in Korean, and has three of the five states of abdominal obesity, hypertension, hunger glycemic disorder, high neutral fat, and low HDL cholesterol at the same time. This paper links the consumer panel data of the Rural Development Agency(RDA) and the medical care data of the National Health Insurance Service(NHIS) to generate a classification model that can be divided into a metabolic disease group and a control group through food consumption characteristics, and attempts to compare the differences. Many existing domestic and foreign studies related to metabolic diseases and food consumption characteristics are disease correlation studies of specific food groups and specific ingredients, and this paper is logistic considering all food groups included in the general diet. We created a classification model using regression, a decision tree-based classification model, and a classification model using XGBoost. Of the three models, the high-precision model is the XGBoost classification model, but the accuracy was not high at less than 0.7. As a future study, it is necessary to extend the observation period for food consumption in the patient group to more than 5 years and to study the metabolic disease classification model after converting the food consumed into nutritional characteristics.

• Journal of Digestive Cancer Reports
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• v.7 no.1
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• pp.8-12
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• 2019

Despite its declining incidence, gastric cancer is globally, still, the third most common cause of cancer-related mortality. Gastric cancer is a heterogeneous disease with diverse pathogenesis and molecular backgrounds. Therefore several systems have been proposed to aid in the classification of gastric adenocarcinoma based on the macroscopic, microscopic and anatomical features of the tumor. However, these classifications did not reflect the pathogenesis of the disease. Recently, genomic analysis has identified several subtypes of gastric adenocarcinoma and a detailed understanding of the molecular biology behind the neoplastic phenotype is possible to develop of more effective therapies. We will describe the existing various classification of gastric cancer and the recently introduced molecular biology and immunological classification.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.178-190
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• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.359-373
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• 2019

Bursaphelenchus xylophilus(Pine wilt disease) is a serious threat to the pine forest in Korea. However, dead wood observation by Pine wilt disease is based on field survey. Therefore, it is difficult to observe large-scale forests due to physical and economic problems. In this paper, high resolution images were obtained using the unmanned aerial vehicle (UAV) in the area where the pine wilt disease recurred. The damaged tree due to pine wilt disease was detected using Artificial Neural Network (ANN), Support Vector Machine (SVM) supervision classification technique. Also, the accuracy of supervised classification results was calculated. After conducting supervised classification on accessible forests, the reliability of the accuracy was verified by comparing the results of field surveys.

• Smart Media Journal
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• v.12 no.7
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.