Threshold Based Image Enhancement Method for Low Contrast X-Ray Images Using CLAHE
[ X ]
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Kırıkkale Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
While 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%.
While 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%.
While 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%.
Açıklama
Anahtar Kelimeler
Low Contrast, İmage Processing, X-Ray İmages, Deep Learning, CLAHE, Low Contrast, İmage Processing, X-Ray İmages, Deep Learning, CLAHE, Electrical Engineering
Kaynak
Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
1
Sayı
3-343
