Skin Disease Classification Using Machine Learning Techniques

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.

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The Effect of Data Normalization on the Performance of Machine Learning Techniques for Heart Disease Classification

Journal of Xidian University ◽

10.37896/jxu14.7/051 ◽

2020 ◽

Vol 14 (7) ◽

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Disease Classification ◽

Machine Learning Techniques ◽

Data Normalization ◽

Learning Techniques

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Efficient Discretization Approaches for Machine Learning Techniques to Improve Disease Classification on Gut Microbiome Composition Data

Advances in Science Technology and Engineering Systems Journal ◽

10.25046/aj050368 ◽

2020 ◽

Vol 5 (3) ◽

pp. 547-556

Author(s):

Hai Thanh Nguyen ◽

Nhi Yen Kim Phan ◽

Huong Hoang Luong ◽

Trung Phuoc Le ◽

Nghi Cong Tran

Keyword(s):

Machine Learning ◽

Gut Microbiome ◽

Disease Classification ◽

Machine Learning Techniques ◽

Microbiome Composition ◽

Composition Data ◽

Learning Techniques

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

Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences ◽

10.53560/ppasa(58-2)692 ◽

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.

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