TY - GEN
T1 - Comparing LBP, HOG and Deep Features for Classification of Histopathology Images
AU - Alhindi, Taha J.
AU - Kalra, Shivam
AU - Ng, Ka Hin
AU - Afrin, Anika
AU - Tizhoosh, Hamid R.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - Medical image analysis has become a topic under the spotlight in recent years. There is a significant progress in medical image research concerning the usage of machine learning. However, there are still numerous questions and problems awaiting answers and solutions, respectively. In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network. Three common image classification methods, including support vector machines, decision trees, and artificial neural networks are used to classify feature vectors obtained by different feature extractors. We use KIMIA Path960, a publicly available dataset of 960 histopathology images extracted from 20 different tissue scans to test the accuracy of classification and feature extractions models used in the study, specifically for the histopathology images. SVM achieves the highest accuracy of 90.52% using local binary patterns as features which surpasses the accuracy obtained by deep features, namely 81.14%.
AB - Medical image analysis has become a topic under the spotlight in recent years. There is a significant progress in medical image research concerning the usage of machine learning. However, there are still numerous questions and problems awaiting answers and solutions, respectively. In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network. Three common image classification methods, including support vector machines, decision trees, and artificial neural networks are used to classify feature vectors obtained by different feature extractors. We use KIMIA Path960, a publicly available dataset of 960 histopathology images extracted from 20 different tissue scans to test the accuracy of classification and feature extractions models used in the study, specifically for the histopathology images. SVM achieves the highest accuracy of 90.52% using local binary patterns as features which surpasses the accuracy obtained by deep features, namely 81.14%.
UR - http://www.scopus.com/inward/record.url?scp=85056501402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056501402&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489329
DO - 10.1109/IJCNN.2018.8489329
M3 - Conference contribution
AN - SCOPUS:85056501402
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
Y2 - 8 July 2018 through 13 July 2018
ER -