An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models

Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely ac...

Full description

Bibliographic Details
Main Authors: null Chaahat, Naveen Kumar Gondhi, Parveen Kumar Lehana
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Healthcare Engineering
Online Access:http://dx.doi.org/10.1155/2021/8113403
id doaj-a67ae811ba5f4ad59bfe3ed9be31ad8b
record_format Article
spelling doaj-a67ae811ba5f4ad59bfe3ed9be31ad8b2021-07-26T00:35:10ZengHindawi LimitedJournal of Healthcare Engineering2040-23092021-01-01202110.1155/2021/8113403An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Modelsnull Chaahat0Naveen Kumar Gondhi1Parveen Kumar Lehana2Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of ElectronicsDermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely accessible experts through mobile-based networks for sharing the images and other related information. Under such circumstances, poor image quality introduced due to the capturing device results in misleading diagnosis. Here, a genetic-algorithm- (GA-) based approach used as an image enhancement technique has been explored to improve the low quality of the dermatological images received from the rural clinic. The diagnosis is performed on the enhanced images using convolutional neural network (CNN) classifier for the identification of the diseases. The scope of this paper is limited to only motion blurred images, which is the most prevalent problem in capturing of the images, specifically when any of the two (device or the object) may move unpredictably. Seven types of skin diseases, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, vascular lesion, and squamous cell carcinoma, have been investigated using ResNet-152 giving an overall accuracy of 87.40% for the blurred images. Use of GA-enhanced images increased the accuracy to 95.85%. The results were further analyzed using a confusion matrix and t-test-based statistical investigations. The advantage of the proposed technique is that it reduces the analysis time and errors due to manual diagnosis. Furthermore, speedy and reliable diagnosis at the earliest stage reduces the risk of developing more severe skin problems.http://dx.doi.org/10.1155/2021/8113403
collection DOAJ
language English
format Article
sources DOAJ
author null Chaahat
Naveen Kumar Gondhi
Parveen Kumar Lehana
spellingShingle null Chaahat
Naveen Kumar Gondhi
Parveen Kumar Lehana
An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models
Journal of Healthcare Engineering
author_facet null Chaahat
Naveen Kumar Gondhi
Parveen Kumar Lehana
author_sort null Chaahat
title An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models
title_short An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models
title_full An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models
title_fullStr An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models
title_full_unstemmed An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models
title_sort evolutionary approach for the enhancement of dermatological images and their classification using deep learning models
publisher Hindawi Limited
series Journal of Healthcare Engineering
issn 2040-2309
publishDate 2021-01-01
description Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely accessible experts through mobile-based networks for sharing the images and other related information. Under such circumstances, poor image quality introduced due to the capturing device results in misleading diagnosis. Here, a genetic-algorithm- (GA-) based approach used as an image enhancement technique has been explored to improve the low quality of the dermatological images received from the rural clinic. The diagnosis is performed on the enhanced images using convolutional neural network (CNN) classifier for the identification of the diseases. The scope of this paper is limited to only motion blurred images, which is the most prevalent problem in capturing of the images, specifically when any of the two (device or the object) may move unpredictably. Seven types of skin diseases, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, vascular lesion, and squamous cell carcinoma, have been investigated using ResNet-152 giving an overall accuracy of 87.40% for the blurred images. Use of GA-enhanced images increased the accuracy to 95.85%. The results were further analyzed using a confusion matrix and t-test-based statistical investigations. The advantage of the proposed technique is that it reduces the analysis time and errors due to manual diagnosis. Furthermore, speedy and reliable diagnosis at the earliest stage reduces the risk of developing more severe skin problems.
url http://dx.doi.org/10.1155/2021/8113403
work_keys_str_mv AT nullchaahat anevolutionaryapproachfortheenhancementofdermatologicalimagesandtheirclassificationusingdeeplearningmodels
AT naveenkumargondhi anevolutionaryapproachfortheenhancementofdermatologicalimagesandtheirclassificationusingdeeplearningmodels
AT parveenkumarlehana anevolutionaryapproachfortheenhancementofdermatologicalimagesandtheirclassificationusingdeeplearningmodels
AT nullchaahat evolutionaryapproachfortheenhancementofdermatologicalimagesandtheirclassificationusingdeeplearningmodels
AT naveenkumargondhi evolutionaryapproachfortheenhancementofdermatologicalimagesandtheirclassificationusingdeeplearningmodels
AT parveenkumarlehana evolutionaryapproachfortheenhancementofdermatologicalimagesandtheirclassificationusingdeeplearningmodels
_version_ 1721282354222202880