Design of Automatic Detection of Erythemato-squamous Diseases Through Threshold-based ABC-FELM Algorithm

Badrinath, N.; Gopinath, G.; Ravichandr, K.S.

doi:10.3923/jai.2013.245.256

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Some of the other interesting methods reported in the literature for the diagnosis of ESD are fuzzy classification [18], neuro-fuzzy inference systems [19], k-means clustering [20], boosting [21] and genetic programming [22]. We find that none of the past works reported in the literature have used Deep learning to the best of our knowledge.…”

Section: Related Workmentioning

confidence: 96%

A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

Putatunda

2019

Preprint

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The diagnosis of the Erythemato-squamous disease (ESD) is accepted as a difficult problem in dermatology. ESD is a form of skin disease. It generally causes redness of the skin and also may cause loss of skin. They are generally due to genetic or environmental factors. ESD comprises six classes of skin conditions namely, pityriasis rubra pilaris, lichen planus, chronic dermatitis, psoriasis, seboreic dermatitis and pityriasis rosea. The automated diagnosis of ESD can help doctors and dermatologists in reducing the efforts from their end and in taking faster decisions for treatment. The literature is replete with works that used conventional machine learning methods for the diagnosis of ESD. However, there isn't much instances of application of Deep learning for the diagnosis of ESD.In this paper, we propose a novel hybrid deep learning approach i.e. Derm2Vec for the diagnosis of the ESD. Derm2Vec is a hybrid deep learning model that consists of both Autoencoders and Deep Neural Networks. We also apply a conventional Deep Neural Network (DNN) for the classification of ESD. We apply both Derm2Vec and DNN along with other traditional machine learning methods on a real world dermatology dataset. The Derm2Vec method is found to be the best performer (when taking the prediction accuracy into account) followed by DNN and Extreme Gradient Boosting.The mean CV score of Derm2Vec, DNN and Extreme Gradient Boosting are 96.92%, 96.65% and 95.80% respectively.

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Section: Related Workmentioning

confidence: 96%

A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

Putatunda

2019

Preprint

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A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

Zhu

et al. 2022

Earthq. Eng. Eng. Vib.

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Classification and Prediction of Erythemato-Squamous Diseases Through Tensor-Based Learning

Badrinath¹,

Gopinath

Ravichandran

et al. 2018

Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci.

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Design of Automatic Detection of Erythemato-squamous Diseases Through Threshold-based ABC-FELM Algorithm

Cited by 5 publications

References 11 publications

A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

Classification and Prediction of Erythemato-Squamous Diseases Through Tensor-Based Learning

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