Extreme Learning Machine Based Diagnosis Models for Erythemato-Squamous Diseases

Xie, Juanying; Ji, Xinyuan; Wang, Mingzhao

doi:10.1007/978-3-030-01078-2_6

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Doctors generally try to differentiate and diagnose diseases by evaluating clinical and pathological parameters together. With the advancements in the biomedical science, researchers are trying to help dermatologists in diagnosing the disease by developing various algorithms in the classification of these diseases (Esteva et al, 2017;Karaca, Sertbaş, & Bayrak, 2018;Xie, Ji, & Wang, 2018) However, only three studies on SOM and SVM have been considered here. In these studies, in 2014 Haryanto and colleagues used the Self-Organizing Map (SOM) method to identify Erythematous-Squamous dermatological diseases by working on the same dataset, which included 231 data sets used for classification purposes as 30 training and 201 test data.…”

Section: Introductionmentioning

confidence: 99%

Dermatolojik Verilerin Öz Düzenleyici Harita ve Destek Vektör Makinaları ile Sınıflandırılması

Fidan¹,

Uzunhisarcikli²,

Çalikuşu³

2019

Afyon Kocatepe University Journal of Sciences and Engineering

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The frequency incidence of dermatological diseases is increasing in parallel with the fact that human skin is exposed to different chemicals. Examined many skin diseases, many of them are similar in shape and appearance, although the reasons for their appearance are different. In dermatology, the differential diagnosis of Erythemato-squamous diseases is frequently encountered by doctors. Doctors try to differentiate and diagnose diseases by evaluating clinical findings and histopathological parameters together. Many researchers have developed different algorithms on the classification and clustering of diseases and data that have been diagnosed from the UCI database. In the present study, unlike previous studies, clinical and histopathological findings of 6 different Erythamo Squamos skin diseases were clustered by applying to SOM network separately. As a result of this clustering process, it is determined that Psoriasis -Cronic Dermatitis and Seborreic Dermatitis -Pitriasis Rosea diseases were found in the same cluster and the diagnoses are confused. In order to prevent this confusion, clinical and histopathological findings of the diseases were clustered by SOM method. Clustering parameters of clinical and histopathological findings were classified with SVM. As a result of the study, it was achieved that the classification of Psoriasis -Cronic Dermatitis diseases was classified as 0.89 with an accuracy of 0.93 and that of Seborreic Dermatitis -Pitriasis Rosea with an accuracy of 0.79 and 0.80.

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Section: Introductionmentioning

confidence: 99%