Open AccessComparative study of Image Classification Algorithms
Author(s) -
Utkarsh Pandey,
Himanshu Jindal,
Ajay Tiwari
Publication year - 2021
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst070120
Subject(s) - artificial intelligence , support vector machine , computer science , naive bayes classifier , decision tree , k nearest neighbors algorithm , machine learning , pattern recognition (psychology) , cross validation , linear discriminant analysis , algorithm
This investigation analyzed five common machine learning techniques for performing image classificationincluded Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Binary Decision Tree(BDT) and Discriminant Analysis (DA). AlexNet deep learning model was utilized to fabricate these machinelearning classifiers. The structure classifiers were executed and assessed by standard execution models ofAccuracy (ACC), Precision (P), Sensitivity (S), Specificity (Spe) and Area Under the ROC Curve (AUC). The fivestrategies were assessed utilizing 2608 histopathological pictures for head and neck cancer. Theexamination was directed utilizing multiple times 10-overlay cross validation. For every strategy, thepre-trained AlexNet network was utilized to separate highlights from the activation layer. The outcomesoutlined that, there was no contrast between the consequences of SVM and KNN. Both have the equivalentand the higher accuracy than others were 99.98 %, though 99.81%, 97.32% and 93.68% for DA, BDT and NB,separately. The current examination shows that the SVM, KNN and DA are the best techniques for classifyingour dataset images.