Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances

Skin conditions influence people of all ages and genders and impose an enormous strain on worldwide public health. For efficient management and medical treatment, skin disorders must be accurately categorized. However, the conventional method of classifying skin conditions can be arbitrary and time-...

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Bibliographic Details
Published in:Applied sciences 2023-06, Vol.13 (13), p.7712
Main Authors: Almutairi, Aeshah, Khan, Rehan Ullah
Format: Article
Language:English
Subjects:
Analysis
Cancer
Care and treatment
Classification
classifiers
Datasets
Decision making
deep learning
Diagnosis
Evidence-based medicine
feature extraction
Histograms
Learning algorithms
Machine learning
Medical imaging
Medical imaging equipment
Medical treatment
Permutations
Public health
Skin cancer
Skin diseases
Support vector machines
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Summary:Skin conditions influence people of all ages and genders and impose an enormous strain on worldwide public health. For efficient management and medical treatment, skin disorders must be accurately categorized. However, the conventional method of classifying skin conditions can be arbitrary and time-consuming, delaying diagnosis and treatment. In this research, we examine the application of traditional machine learning models and conventional image characteristics for the classification of skin cancer based on picture features. Specifically, we employ six feature extraction approaches, which we model using six classical classifiers. To evaluate our approach, we address skin cancer detection as both a seven-class problem and a two-class problem comprising 21 permutations of skin cancer instances. Our experimental results demonstrate that Random Forest achieves the highest performance, followed by Support Vector Machines. Additionally, our analysis reveals that the Edge Histogram and Fuzzy Opponent Histogram feature sets perform best in learning the skin cancer model. Our comprehensive evaluation of various models provides practitioners with valuable insights when selecting appropriate models for similar problems. Our findings demonstrate that acceptable detection performance can be achieved even with simple feature extraction and non-deep classifiers. We argue that classical features are not only easier and faster to extract than deep features but can also be combined with classical machine learning models to save time and valuable resources.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13137712