Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification

2019 ◽  
pp. 313-327
Author(s):  
S. Franchini ◽  
M. C. Terranova ◽  
G. Lo Re ◽  
S. Salerno ◽  
M. Midiri ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Disease Classification ◽  
Support Vector

Long-term Disease Behavior and Surgical Intervention Analysis in Hospitalized Patients with Crohn's Disease in China: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Xiaoyin Bai ◽  
Huimin Zhang ◽  
Gechong Ruan ◽  
Hong Lv ◽  
Yue Li ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Large Scale ◽  
Hospitalized Patients ◽  
Disease Classification ◽  
Real World Data ◽  
Disease Behavior ◽  
Initial Classification

Abstract Background There is lack of real-world data for disease behavior and surgery of Crohn’s disease (CD) from large-scale Chinese cohorts. Methods Hospitalized patients diagnosed with CD in our center were consecutively included from January 2000 to December 2018. Disease behavior progression was defined as the initial classification of B1 to the progression to B2 or B3. Clinical characteristics including demographics, disease classification and activity, medical therapy, development of cancers, and death were collected. Results Overall, 504 patients were included. Two hundred and thirty one (45.8%) patients were initially classified as B1; 30 (13.0%), 71 (30.7%), and 95 (41.1%) of them had disease progression at the 1-year follow-up, 5-year follow-up, and overall, respectively. Patients without location transition before behavior transition were less likely to experience behavior progression. However, patients without previous exposure to a corticosteroid, immunomodulator, or biological agent had a greater chance of experiencing behavior progression. When the long-term prognosis was evaluated, 211 (41.9%) patients underwent at least one CD-related surgery; 108 (21.4%) and 120 (23.8%) of these patients underwent surgery before and after their diagnosis, respectively. An initial classification as B1, no behavior transition, no surgery prior to diagnosis, and previous corticosteroid exposure during follow-up were associated with a lower risk of undergoing surgery. Conclusions This study depicts the clinical features and factors associated with behavior progression and surgery among hospitalized CD patients in a Chinese center. Behavior progression is associated with a higher probability of CD-related surgery, and strengthened therapies are necessary for them in the early phase.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

Automatic Fruit Disease Classification Using Images

2014 ◽  
pp. 82-100 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Support Vector Machine ◽  
Apple Scab ◽  
Texture Features ◽  
Disease Classification ◽  
Economic Losses ◽  
Support Vector ◽  
Test Case ◽  
Agricultural Industry ◽  
Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

scholarly journals Classifying Crohn’s disease into colon-involving versus non-colon-involving groups is a better predictor of clinical outcomes than the Montreal classification

2020 ◽  
Vol 13 ◽  
pp. 175628482096873
Author(s):  
Si-Nan Lin ◽  
Dan-Ping Zheng ◽  
Yun Qiu ◽  
Sheng-Hong Zhang ◽  
Yao He ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Abdominal Surgery ◽  
Clinical Outcomes ◽  
Protective Factor ◽  
Age Of Onset ◽  
Disease Classification ◽  
Major Abdominal Surgery ◽  
Montreal Classification

Background: A suitable disease classification is essential for individualized therapy in patients with Crohn’s disease (CD). Although a potential mechanistic classification of colon-involving and non-colon-involving disease was suggested by recent genetic and microbiota studies, the clinical implication has seldom been investigated. We aimed to explore the association of this colonic-based classification with clinical outcomes in patients with CD compared with the Montreal classification. Methods: This was a retrospective study of CD patients from a tertiary referral center. Patients were categorized into colon-involving and non-colon-involving disease, and according to the Montreal classification. Clinico-demographic data, medications, and surgeries were compared between the two classifications. The primary outcome was the need for major abdominal surgery. Results: Of 934 patients, those with colonic involvement had an earlier median (interquartile range) age of onset [23.0 (17.0–30.0) versus 26.0 (19.0–35.0) years, p = 0.001], higher frequency of perianal lesions (31.2% versus 14.5%, p < 0.001) and extraintestinal manifestations (21.8% versus 14.5%, p = 0.010), but lower frequency of stricture (B2) (16.3% versus 24.0%, p = 0.005), than those with non-colon-involving disease. Colon-involving disease was a protective factor against major abdominal surgery [hazard ratio, 0.689; 95% confidence interval (CI), 0.481–0.985; p = 0.041]. However, patients with colon-involving CD were more prone to steroids [odds ratio (OR), 1.793; 95% CI, 1.206–2.666; p = 0.004] and azathioprine/6-mercaptopurine (AZA/6-MP) treatment (OR, 1.732; 95% CI, 1.103–2.719; p = 0.017) than were patients with non-colon-involving disease. The Montreal classification was not predictive of surgery or steroids and AZA/6-MP treatment. Conclusion: This study supports the rationale for disease classification based on the involvement of colon. This new classification of CD is a better predictor of clinical outcomes than the Montreal classification.

scholarly journals Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach

2016 ◽  
Vol 55 (1) ◽  
pp. 101-115 ◽  
Author(s):  
Sk. Saddam Ahmed ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Dimitra Sifaki-Pistolla ◽  
Dana Bălas-Timar ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Disease Classification ◽  
Fuzzy Partitioning ◽  
Neuro Fuzzy

scholarly journals An image-based gangrene disease classification

2020 ◽  
Vol 10 (6) ◽  
pp. 6001
Author(s):  
Pramod Sekharan Nair ◽  
Tsrity Asefa Berihu ◽  
Varun Kumar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Image Features ◽  
The Body ◽  
Disease Classification ◽  
Support Vector ◽  
Body Parts ◽  
Intermediate Layers ◽  
The Given

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.

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