TY - GEN
T1 - Computer-Aided Diagnosis of Diabetic Foot Ulcers
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
AU - Xian, Qixin
AU - Chibeze, Precious
AU - Li, Guojie
AU - Abdullah, Muhammad Amirul
AU - Yap, Eng Hwa
AU - Omar, Zaid
AU - Majeed, Anwar P.P.Abdul
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Diabetic foot ulcers (DFUs) can lead to severe infections and amputations if not detected early. This study aims to identify and extract features from DFU datasets using a class of deep learning approach known as feature-based transfer learning for improved diagnosis. The dataset contains normal and abnormal feet images with existing ulcers. A pre-trained convolutional neural network model, viz. VGG19 is utilized to extract discriminative features from the images. Two supervised learning methods, namely k-Nearest Neighbours (kNN) and Logistic Regression (LR), are then trained on these images to classify them as normal or abnormal. The data is split into 70:15:15 train, validation and test sets. It was illustrated from the study that the VGG19 + LR pipeline is able to distinguish the classes of DFUs well, suggesting its promising potential for computer-aided diagnosis.
AB - Diabetic foot ulcers (DFUs) can lead to severe infections and amputations if not detected early. This study aims to identify and extract features from DFU datasets using a class of deep learning approach known as feature-based transfer learning for improved diagnosis. The dataset contains normal and abnormal feet images with existing ulcers. A pre-trained convolutional neural network model, viz. VGG19 is utilized to extract discriminative features from the images. Two supervised learning methods, namely k-Nearest Neighbours (kNN) and Logistic Regression (LR), are then trained on these images to classify them as normal or abnormal. The data is split into 70:15:15 train, validation and test sets. It was illustrated from the study that the VGG19 + LR pipeline is able to distinguish the classes of DFUs well, suggesting its promising potential for computer-aided diagnosis.
KW - Computer Aided Diagnosis
KW - Deep Learning
KW - Diabetic Foot Ulcers
KW - Feature-based Transfer Learning
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/85211337394
U2 - 10.1007/978-3-031-70687-5_8
DO - 10.1007/978-3-031-70687-5_8
M3 - Conference contribution
AN - SCOPUS:85211337394
SN - 9783031706868
T3 - Lecture Notes in Networks and Systems
SP - 72
EP - 77
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P. P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 December 2023 through 8 December 2023
ER -