Hybrid Classifier to Classify the Finger Nail Abnormalities

V., M. Renuka Devi Thahira Banu

doi:10.17762/itii.v9i1.168

Cited by 2 publications

(2 citation statements)

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“…In the work of Thahira Banu and Devi (2021), GLCM with a filter-based approach was used for feature extraction. A new hybrid method was used for classification combining k-NN and SVM models.…”

Section: Related Workmentioning

confidence: 99%

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Ali

Kuyucuoglu

KÖSEOĞLU

et al. 2023

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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Nail diseases are disorders that can have serious effects on human quality of life. With the developing computational methods and technology, anomalies on the nail may be detected quickly and in a non-invasive way. This study proposes a model that provides better performance by combining the results of different deep learning networks with the ensemble learning method. The performance of 7 different deep learning architectures was examined using a database containing 17 disease classes. The proposed method achieved 75 % accuracy, resulting in significant increases in precision and recall metrics compared to individual deep-learning architectures. Thanks to a mobile application that will be developed, the proposed model for large-scale screening may be used as an assistive decision support system for medical professionals. When the results are observed, we predict that early detection of nail diseases (in a remote way) on the hand, which is one of our most used limbs, can reduce hospital visits and costs. In addition, the proposed method can be integrated into dermatoscopy devices used for skin diseases and mole analysis.

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Section: Related Workmentioning

confidence: 99%

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Ali

Kuyucuoglu

KÖSEOĞLU

et al. 2023

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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“…There are several studies in the literature for fingernail disease classification. Banu and Devi compared the performance of various classifiers, such as SVM and KNN, based on an image dataset of eight types of abnormalities (Thahira Banu & Devi, 2021). Indi and Patil analyzed the color, shape, and features of fingernails and applied SVM, KNN, and ANN classifiers for predicting diseases (Indi & Patil, 2019) (Yani & others, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosing Diseases From Fingernail Images

CAN

Işık

2022

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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This paper investigates how people's finger and nail appearance helps diagnose various diseases, such as Darier's disease, Muehrcke's lines, alopecia areata, beau's lines, bluish nails, and clubbing, by image processing and deep learning techniques. We used a public dataset consisting of 17 different classes with 655 samples. We divided the dataset into three folds based on a widely used rule, the 0.7:0.2:0.1, for training, validation, and testing purposes. We tested the EfficientNet-B2 model for performance evaluation purposes by using Noisy-Student weights by setting the batch size and epochs as 32 and 1000. The model achieves a 72% accuracy score and 91% AUC score for test samples to detect fingernail diseases. The empirical findings in this study provide a new understanding that the EfficientNet-B2 model can categorize nail disease types through numerous classes.

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Hybrid Classifier to Classify the Finger Nail Abnormalities

Cited by 2 publications

References 0 publications

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Diagnosing Diseases From Fingernail Images

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