Feature Extraction for Medical Image Classification: A Novel Statistical Approach
| dc.contributor.author | Mohmed, Ashraf Rafa | |
| dc.contributor.author | Celik, Yuksel | |
| dc.date.accessioned | 2024-09-29T16:06:37Z | |
| dc.date.available | 2024-09-29T16:06:37Z | |
| dc.date.issued | 2023 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description.abstract | Medical image classification is an increasingly important area of research, with the need to represent images computationally often posing significant challenges due to the large amounts of data and processing power required. A new approach for image classification in the healthcare domain has been developed in this study, called ASPS_HC, which seeks to obtain higher discrimination among different classes by identifying the most impactful features within the data's Upper and Lower Limit outlier regions. This is achieved through the use of various statistical measures, including the Coefficient of Variance (CV), to create 48 features that represent each image. An experiment was conducted on a dataset of 5,540 diabetic retinopathy images in the Gaussian formula, acquired from Kaggle. The proposed ASPS_HC approach yielded three main advantages over the previous ASPS method for feature extraction: the average rank of the features was increased by 200%, the run time was reduced by 23.30%, and the number of features required was decreased by 50%. As a result, the features extracted using ASPS_HC produced significantly higher accuracy in both the Artificial Neural Network and Random Forest models, with an increase of 1.91% for the former and 1.36% for the latter. | en_US |
| dc.identifier.doi | 10.18280/ts.400232 | |
| dc.identifier.endpage | 733 | en_US |
| dc.identifier.issn | 0765-0019 | |
| dc.identifier.issn | 1958-5608 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.scopus | 2-s2.0-85162187627 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.startpage | 727 | en_US |
| dc.identifier.uri | https://doi.org/10.18280/ts.400232 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/6933 | |
| dc.identifier.volume | 40 | en_US |
| dc.identifier.wos | WOS:000996210200032 | en_US |
| dc.identifier.wosquality | Q4 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Int Information & Engineering Technology Assoc | en_US |
| dc.relation.ispartof | Traitement Du Signal | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | ASPS approach | en_US |
| dc.subject | classification medical images | en_US |
| dc.subject | diabetic retinopathy | en_US |
| dc.subject | features extraction | en_US |
| dc.subject | features selection | en_US |
| dc.subject | healthcare | en_US |
| dc.title | Feature Extraction for Medical Image Classification: A Novel Statistical Approach | en_US |
| dc.type | Article | en_US |
