A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features

Abstract

The main purpose of this study is to observe the importance of machine vision (MV) approach for the identification of five types of skin cancers, namely, actinic-keratosis, benign, solar-lentigo, malignant, and nevus. The 1000 (200 × 5) benchmark image datasets of skin cancers are collected from the International Skin Imaging Collaboration (ISIC). The acquired ISIC image datasets were transformed into texture feature dataset that was a combination of first-order histogram and gray level co-occurrence matrix (GLCM) features. For the skin cancer image, a total of 137,400 (229 × 3 x 200) texture features were acquired on three nonover-lapping regions of interest (ROIs). Principal component analysis (PCA) clustering approach was employed for reducing the dimension of feature dataset. Each image acquired twenty most discriminate features based on two different approaches of statistical features such as average correlation coefficient plus probability of error (ACC + POE) and Fisher (Fis). Furthermore, a correlation-based feature selection (CFS) approach was employed for feature reduction, and optimized 12 features were acquired. Furthermore, a classification algorithm naive bayes (NB), Bayes Net (BN), LMT Tree, and multilayer perception (MLP) using 10 K-fold cross-validation approach were employed on optimized feature datasets and the overall accuracy achieved by MLP is 97.1333%.

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

“…The hybrid features are extracted to classify skin cancer in a machine vision approach. The GLCM and first-order histogram features are combined, moreover, dimensions get reduced with the help of PCA [ 103 ]. The researchers extract modified ABCD by using cumulative level difference mean and prominent attributes selected by the Eigenvector centrality feature ranking and selection approach [ 104 ].…”

A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features

“…is work is available in SSRN as a preprint article; it offers immediate access but has not been peer-reviewed [40]. Link/references Methodology/technique Classification OCA (%) Kumar et al [12] R-CNN Artificial neural network (ANN) 95 Esteva et al [13] GoogleNet inspection V-3 Convolutional neural network 72.1 ± 0.9 Shena et al [14] ResNet-34 model Artificial neural network (ANN) 78.4 Yasir et al [16] Computer vision Artificial neural network (ANN) 90 Haenssle et al [24] Google's inception v4 CNN Deep CNN 86.6 Kawahara et al [25] Image…”

Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals

“…Artificial intelligence (AI) has been widely used in the health area to assist health professionals in decision making with a focus on screening, classification, and diagnostics, among other possibilities. The work reported in [11] developed a computer vision approach for the classification of skin cancer using hybrid texture features. They achieved an overall accuracy of 97.13% using a multi-layer perceptron (MLP).…”