@article{article_1728399, title={DETECTION OF HEALTH BEHAVIOR IMPACT AND COVID-19 WITH BLACK FUNGUS (MUCORMYCOSIS), USING A NOVEL HYBRID MODEL OF IMAGE SEGMENTATION, INCORPORATING “WHALE OPTIMIZATION ALGORITHM”}, journal={International Anatolia Academic Online Journal Health Sciences}, volume={11}, pages={582–596}, year={2025}, author={İnce, Özgür}, keywords={Kara Mantar, Balina Optimizasyon Algoritması, Hibrit Model, COVID-19, Görüntü Segmentasyonu, Sağlık Davranışı}, abstract={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.}, number={1}, publisher={Abdülkadir IŞIK}