Threshold Based Image Enhancement Method for Low Contrast X-Ray Images Using CLAHE

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dc.contributor.authorHatipoğlu, Buğra
dc.contributor.authorKaragöz, Prof. Dr. İrfan
dc.contributor.authorİnal, Mikail
dc.date.accessioned2025-01-21T14:29:13Z
dc.date.available2025-01-21T14:29:13Z
dc.date.issued2022
dc.description.abstractWhile a large dataset in deep learning may seem like a positive factor, it may not always produce good results. Image quality is one of the factors that directly affects model performance, which in turn affects the quality of training. In this study, the effect of low contrast X-ray images on the detection of Covid-19 and pneumonia was investigated. Because the details are extremely important in the detection of these diseases. If the images are low contrast, it can cause some details to be missed in the detection of the disease. This problem can be solved using adaptive histogram methods such as CLAHE. The CLAHE method can apply various filters to low contrast images to bring them to the desired levels. The data set contains 8849 human lung X-ray images. The Vgg16 model was used for training. Vgg16 is a state of the art model architecture in deep learning. The image dimensions are 150x150. Classification performed before low-contrast images were filtered achieved 95.22% accuracy. After filtering based on the threshold value, accuracy increased to 97.35%. In the next stage, by searching for the best values for the parameters, accuracy was increased to 97.86%.
dc.description.abstractWhile a large dataset in deep learning may seem like a positive factor, it may not always produce good results. Image quality is one of the factors that directly affects model performance, which in turn affects the quality of training. In this study, the effect of low contrast X-ray images on the detection of Covid-19 and pneumonia was investigated. Because the details are extremely important in the detection of these diseases. If the images are low contrast, it can cause some details to be missed in the detection of the disease. This problem can be solved using adaptive histogram methods such as CLAHE. The CLAHE method can apply various filters to low contrast images to bring them to the desired levels. The data set contains 8849 human lung X-ray images. The Vgg16 model was used for training. Vgg16 is a state of the art model architecture in deep learning. The image dimensions are 150x150. Classification performed before low-contrast images were filtered achieved 95.22% accuracy. After filtering based on the threshold value, accuracy increased to 97.35%. In the next stage, by searching for the best values for the parameters, accuracy was increased to 97.86%.
dc.identifier.dergipark1203617
dc.identifier.doi10.29137/umagd.1203617
dc.identifier.issn1308-5514
dc.identifier.issue3-343
dc.identifier.startpage350
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/2768641
dc.identifier.urihttps://dergipark.org.tr/tr/pub/umagd/issue/74185/1203617
dc.identifier.urihttps://doi.org/10.29137/umagd.1203617
dc.identifier.urihttps://hdl.handle.net/20.500.12587/20427
dc.identifier.volume1
dc.language.isoen
dc.publisherKırıkkale Üniversitesi
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectLow Contrast
dc.subjectİmage Processing
dc.subjectX-Ray İmages
dc.subjectDeep Learning
dc.subjectCLAHE
dc.subjectLow Contrast
dc.subjectİmage Processing
dc.subjectX-Ray İmages
dc.subjectDeep Learning
dc.subjectCLAHE
dc.subjectElectrical Engineering
dc.titleThreshold Based Image Enhancement Method for Low Contrast X-Ray Images Using CLAHE
dc.title.alternativeThreshold Based Image Enhancement Method for Low Contrast X-Ray Images Using CLAHE
dc.typeArticle

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