Authors :
Vasudeva R; Dr. S N Chandrashekara
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/bde45m78
DOI :
https://doi.org/10.5281/zenodo.8413591
Abstract :
The most anticipated procedures in the
present digital era are image retrieval from vast
libraries of health care sector data. The objective of this
work is to use machine learning techniques to efficiently
classify medical images from larger healthcare datasets.
In this paper, features are retrieved using GLCM (Grey
Level Co-occurrence Matrix) from five different classes
of medical images using image properties including
dissimilarity, correlation, homogeneity, contrast, ASM,
and energy. To extract the attributes utilizing the
layers, the photographs are looked at from a variety of
angles (0, 45, 90, and 135). Input is made to the trendiest
Machine Learning (ML) models using the received
feature vectors, that is Random Forest (RF) and
Support vector machines (SVM) are used to classify
images. Performance evaluation is carried out by
contrasting and assessing the experimental results of
SVM and Random Forest, two algorithms produces
classification accuracy of 95.22% and 96.37%,
respectively. When picture classification is done on
bigger healthcare datasets, the Precision, Recall, and
F1-Score are compared as well, and they are more
accurate. In comparison, the feature extraction
employing GLCM and ML models can extract better
medical image features and achieve higher classification
accuracy.
Keywords :
Grey Level Co-occurrence Matrix, Cloud Computing, Random Forest (RF) and Support Vector Machines (SVM), Content-Based Image Retrieval.
The most anticipated procedures in the
present digital era are image retrieval from vast
libraries of health care sector data. The objective of this
work is to use machine learning techniques to efficiently
classify medical images from larger healthcare datasets.
In this paper, features are retrieved using GLCM (Grey
Level Co-occurrence Matrix) from five different classes
of medical images using image properties including
dissimilarity, correlation, homogeneity, contrast, ASM,
and energy. To extract the attributes utilizing the
layers, the photographs are looked at from a variety of
angles (0, 45, 90, and 135). Input is made to the trendiest
Machine Learning (ML) models using the received
feature vectors, that is Random Forest (RF) and
Support vector machines (SVM) are used to classify
images. Performance evaluation is carried out by
contrasting and assessing the experimental results of
SVM and Random Forest, two algorithms produces
classification accuracy of 95.22% and 96.37%,
respectively. When picture classification is done on
bigger healthcare datasets, the Precision, Recall, and
F1-Score are compared as well, and they are more
accurate. In comparison, the feature extraction
employing GLCM and ML models can extract better
medical image features and achieve higher classification
accuracy.
Keywords :
Grey Level Co-occurrence Matrix, Cloud Computing, Random Forest (RF) and Support Vector Machines (SVM), Content-Based Image Retrieval.
