Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm

Abstract

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.

Rice Disease Recognition Using Transfer Learning Xception Convolutional Neural Network

“…The features that are widely used are Gray level co-occurrence matrices (GLCM) [4]- [7]. Meanwhile, ML classifiers used in classification tasks such as K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression(LR), backpropagation Artificial Neural Network (BPNN) and Support Vector Machine (SVM) [4]- [6], [8]- [10]. However, currently, the role of ML is starting to be replaced by DL, this is proven in research [11], where it is proven that the performance of DL methods, especially CNN, has succeeded in getting much better accuracy compared to ML methods such as KNN, NB, SVM, and BPNN based on accuracy, specificity, recall, and F1-score.…”

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

“… UCSC Xena functional genomics browser 25,57 RF, LR, XGB kidney renal clear cell carcinoma RF = 59 % LR = 69 % XGB = 76 % [ 59 ] Proposed Multi-view Factorization Au-toEncoder for integrating multi-omic data with domain knowledge. TCGA-BLCA, TCGA-LGG 10,546 SVM, AdaBoost, DT, NB, RF, Variational Autoencoder (VAE), Adversarial Autoencoder (AAE) Cancer SVM = 68 % DT = 57 % NB = 63 % RF = 67 % VAE = 56 % AAE = 69 % [ 1 ] Benign and Malignant Skin Disease Prediction Using ML ISIC 3297, 1649, 825, and 210 images KNN Skin cancer 79.24 %, 79.39 %, 83.63 %, and 100 % for, respectively 3297, 1649, 825, and 210 …”

“…Multi-omics aims to integrate multiple omics such as genomes, transcriptomics, proteomics, metabolomics, and other such studies to understand the complex molecular interactions within cells. As a result of this recognition, testing and experiments based on multi-omics approaches have made breakthroughs lately in several medical science fields, such as skin disease, cancer research [ 1 ], microbiome analysis, and drug development. Hence the detailed research on multi-omics studies provides insight into complex biological systems and as a result, researchers are encouraged to find out the novel treatment targets, biomarker development and precision medicine.…”

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

“…SVM effectively handles model complexity, performs well in high-feature spaces, and efficiently deals with overfitting. Overall, SVM is a powerful and flexible method for classifying diverse datasets, even imbalances [18]- [21]. SVM can also be applied as a deep learning (DL) model classifier layer.…”