Brain Disease Classification and Progression Using Machine Learning Techniques

2013 ◽  
pp. 3-32 ◽  
Author(s):  
Bo Cheng ◽  
Chong-Yaw Wee ◽  
Manhua Liu ◽  
Daoqiang Zhang ◽  
Dinggang Shen
Keyword(s):  
Machine Learning ◽  
Brain Disease ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques

Applications of Machine Learning Techniques in Disease Classification From Medical Images

2018 ◽  
pp. 318-337 ◽  
Author(s):  
Bikesh Kumar Singh ◽  
Satya Eswari Jujjavarapu
Keyword(s):  
Machine Learning ◽  
Medical Images ◽  
Radial Basis Function Network ◽  
Objective Evaluation ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Future Challenges ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning

Machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), radial basis function network (RBFN), random forest (RF), naive Bayes classifier, etc. have gained much attention in recent years due to their widespread applications in diverse fields. This chapter is focused on providing a comprehensive insight of various techniques employed for key areas of medical image processing and analysis. Different applications covered in this chapter include feature extraction, feature selection, and cancer classification in medical images. The authors present current practices and evaluation measures used for objective evaluation of different machine learning methods in context to above-mentioned applications. Various factors associated with acceptance/rejection of such automated systems by medical research community are discussed. The authors also discuss how the interaction between automated analysis systems and medical professionals can be improved for its acceptance in clinical practice. They conclude the chapter by presenting research gaps and future challenges.

Skin Disease Classification Using Machine Learning Techniques

2021 ◽  
pp. 597-608
Author(s):  
Mohammad Ashraful Haque Abir ◽  
Golam Kibria Anik ◽  
Shazid Hasan Riam ◽  
Mohammed Ariful Karim ◽  
Azizul Hakim Tareq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Skin Disease ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques

Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques

2021 ◽  
Vol 58 (2) ◽  
pp. 1-10
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Jamal Abdul Nasir ◽  
Arooj Fatima ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Space ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Corn Seed ◽  
Area Of Interest ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Correlation Based Feature Selection ◽  
Conventional Machine

The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.

scholarly journals Texture based Leaf Disease Classification using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 956-961
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Textural Features ◽  
Leaf Disease ◽  
Learning Techniques ◽  
Segmented Images ◽  
Agricultural Applications

Machine learning techniques has emerged as a potential field in many of present day agricultural applications. One of these applications is the identification and classification of leaf diseases. In this paper, a triangular based and OTSU based methods are applied for segmentation, Textural features primarily based on GLCM are obtained for these segmented images using kmeans clustering technique, further classification of different leaf disease is performed using an SVM based classification. The proposed method resulted in an overall classification accuracy of 70% using the triangular based segmentation with an AUC of 0.63.

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