A Deep Hybrid Learning Approach For Nail Diseases Classification

Dalia A. Alzahrani; Rahaf R. Alhajri; Nouf A. Alali; Maram L. Alfaraj; Danah S. Alotaibi; Alaa Y. Alahmadi

doi:10.1109/ICCIT58132.2023.10273947

A Deep Hybrid Learning Approach For Nail Diseases Classification

Dalia A. Alzahrani^*
, Rahaf R. Alhajri
, Nouf A. Alali
, Maram L. Alfaraj
, Danah S. Alotaibi
, Alaa Y. Alahmadi

^*Corresponding author for this work

Computer Science Department

Imam Abdulrahman Bin Faisal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

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%.

Original language	English
Title of host publication	2023 3rd International Conference on Computing and Information Technology, ICCIT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	325-333
Number of pages	9
ISBN (Electronic)	9798350321487
DOIs	https://doi.org/10.1109/ICCIT58132.2023.10273947
State	Published - 2023
Event	3rd International Conference on Computing and Information Technology, ICCIT 2023 - Tabuk, Saudi Arabia Duration: 13 Sep 2023 → 14 Sep 2023

Publication series

Name	2023 3rd International Conference on Computing and Information Technology, ICCIT 2023

Conference

Conference	3rd International Conference on Computing and Information Technology, ICCIT 2023
Country/Territory	Saudi Arabia
City	Tabuk
Period	13/09/23 → 14/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Deep Hybrid Learning
Feature Extraction
Fingernail Disease Prediction
Image Processing

Access to Document

10.1109/ICCIT58132.2023.10273947

Cite this

Alzahrani, D. A., Alhajri, R. R., Alali, N. A., Alfaraj, M. L., Alotaibi, D. S., & Alahmadi, A. Y. (2023). A Deep Hybrid Learning Approach For Nail Diseases Classification. In 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023 (pp. 325-333). (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCIT58132.2023.10273947

Alzahrani, Dalia A. ; Alhajri, Rahaf R. ; Alali, Nouf A. et al. / A Deep Hybrid Learning Approach For Nail Diseases Classification. 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 325-333 (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023).

@inproceedings{792cf3c36a9242ea94a22401758515ab,

title = "A Deep Hybrid Learning Approach For Nail Diseases Classification",

abstract = "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\%.",

keywords = "Deep Hybrid Learning, Feature Extraction, Fingernail Disease Prediction, Image Processing",

author = "Alzahrani, \{Dalia A.\} and Alhajri, \{Rahaf R.\} and Alali, \{Nouf A.\} and Alfaraj, \{Maram L.\} and Alotaibi, \{Danah S.\} and Alahmadi, \{Alaa Y.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd International Conference on Computing and Information Technology, ICCIT 2023 ; Conference date: 13-09-2023 Through 14-09-2023",

year = "2023",

doi = "10.1109/ICCIT58132.2023.10273947",

language = "English",

series = "2023 3rd International Conference on Computing and Information Technology, ICCIT 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "325--333",

booktitle = "2023 3rd International Conference on Computing and Information Technology, ICCIT 2023",

}

Alzahrani, DA, Alhajri, RR, Alali, NA, Alfaraj, ML, Alotaibi, DS & Alahmadi, AY 2023, A Deep Hybrid Learning Approach For Nail Diseases Classification. in 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023, Institute of Electrical and Electronics Engineers Inc., pp. 325-333, 3rd International Conference on Computing and Information Technology, ICCIT 2023, Tabuk, Saudi Arabia, 13/09/23. https://doi.org/10.1109/ICCIT58132.2023.10273947

A Deep Hybrid Learning Approach For Nail Diseases Classification. / Alzahrani, Dalia A.; Alhajri, Rahaf R.; Alali, Nouf A. et al.
2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 325-333 (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

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 -

Alzahrani DA, Alhajri RR, Alali NA, Alfaraj ML, Alotaibi DS, Alahmadi AY. A Deep Hybrid Learning Approach For Nail Diseases Classification. In 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 325-333. (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023). doi: 10.1109/ICCIT58132.2023.10273947

A Deep Hybrid Learning Approach For Nail Diseases Classification

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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