TY  - JOUR
T1  - DETECTION OF HEALTH BEHAVIOR IMPACT AND COVID-19 WITH BLACK FUNGUS (MUCORMYCOSIS), USING A NOVEL HYBRID MODEL OF IMAGE SEGMENTATION, INCORPORATING “WHALE OPTIMIZATION ALGORITHM”
TT  - "Balina Optimizasyon Algoritması"nı İçeren Yeni Bir Hibrit Görüntü Segmentasyon Modeli Kullanılarak Siyah Mantar (Mukormikozis) ile Sağlık Davranışı Etkisinin ve Covid-19'un Tespiti
AU  - İnce, Özgür
PY  - 2025
DA  - August
Y2  - 2025
JF  - International Anatolia Academic Online Journal Health Sciences
JO  - IAAOJH
PB  - Abdülkadir IŞIK
WT  - DergiPark
SN  - 2148-3159
SP  - 582
EP  - 596
VL  - 11
IS  - 1
LA  - en
AB  - Covid-19 has also been linked to other fungal and bacterial infections which can lead to various serious health-related conditions, impacting the health behavior of the associated individuals. In this regard, the incorporation of black fungus (BF), also known as mucormycosis, is found to be common. Therefore, the early detection of covid-19 patient with BF is considered to be crucial to prevent any serious damages. Thus, the aim of this study is to propose an effective novel hybrid model of image segmentation for automated and early diagnosis of covid-19 with BF. For this purpose, a hybrid model was proposed which was applied to the processes images. However, the “Otsu’s thresholding” and the “adaptive method” were used for the segmentation of images and the “Whale Optimization Algorithm” (WOA) was used for optimization and performance analysis was conducted. The results obtained from this study showed that the proposed hybrid model has high percentage of accuracy, precision, sensitivity, recall and specificity as compared to other models. This study also provides different health behavior implications within the context of covid-19 patients with BF.
KW  - Black Fungus
KW  - Whale Optimization Algorithm
KW  - Hybrid Model
KW  - Covid-19
KW  - Image Segmentation
KW  - Health Behaviour
N2  - Covid-19 çeşitli ciddi sağlık sorunlarına yol açabilen ve ilişkili bireylerin sağlık davranışlarını etkileyebilen çeşitli mantar ve bakteri enfeksiyonlarıyla da ilişkilendirilmiştir. Bu bağlamda, mukormikoz olarak da bilinen siyah mantarın (BF) dahil edilmesinin yaygın olduğu bulunmuştur. Bu nedenle, BF'li covid-19 hastasının erken tespiti, ciddi hasarları önlemek için çok önemli kabul edilir. Bu nedenle, bu çalışmanın amacı, BF'li covid-19'un otomatik ve erken teşhisi için etkili bir yeni hibrit görüntü segmentasyon modeli önermektir. Bu amaçla, işlem görüntülerine uygulanan bir hibrit model önerildi. Ancak, görüntülerin segmentasyonu için "Otsu'nun eşikleme" ve "uyarlanabilir yöntem" kullanıldı ve optimizasyon için "Balina Optimizasyon Algoritması" (WOA) kullanıldı ve performans analizi yapıldı. Bu çalışmadan elde edilen sonuçlar, önerilen hibrit modelin diğer modellere kıyasla yüksek oranda doğruluk, kesinlik, duyarlılık, geri çağırma ve özgüllüğe sahip olduğunu göstermiştir. Bu çalışma aynı zamanda BF'li covid-19 hastaları bağlamında farklı sağlık davranışı çıkarımları da sunmaktadır.
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UR  - https://dergipark.org.tr/tr/pub/iaaojh/issue//1728399
L1  - https://dergipark.org.tr/tr/download/article-file/4995816
ER  -