TY - GEN
T1 - A Deep Hybrid Learning Approach For Nail Diseases Classification
AU - Alzahrani, Dalia A.
AU - Alhajri, Rahaf R.
AU - Alali, Nouf A.
AU - Alfaraj, Maram L.
AU - Alotaibi, Danah S.
AU - Alahmadi, Alaa Y.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In medical domains, the appearance of fingernails can provide clues to underlying systemic diseases or nutritional imbalance; the neglection of such clues could lead to unwanted health complications and less chance for recovery. In this paper, a Deep Hybrid Learning (DHL) approach was proposed to detect nail-based diseases, where a Deep Learning (DL) model is used for feature extraction, and a traditional Machine Learning (ML) classifier is used for classification. The aim is to classify three nail diseases: melanoma, beau's nails, and eczema, in addition to healthy nails. Further, the proposed approach is compared to the transfer learning approach, where a pre-trained model is used for feature extraction and classification. The experiment results indicate that the DHL approach is superior to the transfer learning approach. Specifically, the architecture where the DenseNet201 pre-trained model is used for feature extraction and the SGDClassifier is used for classification, as it achieved an accuracy of 94%.
AB - In medical domains, the appearance of fingernails can provide clues to underlying systemic diseases or nutritional imbalance; the neglection of such clues could lead to unwanted health complications and less chance for recovery. In this paper, a Deep Hybrid Learning (DHL) approach was proposed to detect nail-based diseases, where a Deep Learning (DL) model is used for feature extraction, and a traditional Machine Learning (ML) classifier is used for classification. The aim is to classify three nail diseases: melanoma, beau's nails, and eczema, in addition to healthy nails. Further, the proposed approach is compared to the transfer learning approach, where a pre-trained model is used for feature extraction and classification. The experiment results indicate that the DHL approach is superior to the transfer learning approach. Specifically, the architecture where the DenseNet201 pre-trained model is used for feature extraction and the SGDClassifier is used for classification, as it achieved an accuracy of 94%.
KW - Deep Hybrid Learning
KW - Feature Extraction
KW - Fingernail Disease Prediction
KW - Image Processing
UR - https://www.scopus.com/pages/publications/85175444999
U2 - 10.1109/ICCIT58132.2023.10273947
DO - 10.1109/ICCIT58132.2023.10273947
M3 - Conference contribution
AN - SCOPUS:85175444999
T3 - 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023
SP - 325
EP - 333
BT - 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Computing and Information Technology, ICCIT 2023
Y2 - 13 September 2023 through 14 September 2023
ER -
