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

• 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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13137712

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.