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- W4380632258 abstract "The greatest threat to humanity is COVID-19, which has a global impact on billions of people. For important judgment and disease control, therapeutic imaging, such as Computed Tomography (CT), has a lot of potential as an alternative to the Real Time Reverse Transcription–Polymerase Chain Reaction (RT-PCR) test. Automatic image segmentation is therefore highly sought after as a decision support system. Image dissection is dividing a figure into sections entrenched on a set of criteria. In this study, a dataset on COVID CT scan is analyzed using the proposed method of Histogram Binning-Based on Fuzzy K-Means Clustering (HBFKM) with the existing two main cluster methods namely Fuzzy_K-Means (FKM) and Possibilistic_Fuzzy_C-Means (PFCM) and are utilized throughout the segmentation step to segment the images. The findings specify that related to the other approaches under study, the suggested method Histogram Binning-Based on Fuzzy K-Means Clustering offers the highest accuracy and reliability with 85.08 and 85.28% precision. Additionally, the outcomes demonstrate that the proposed approach has the maximum accuracy with a specificity rate of 85.18% ratio. And lastly, the proposed technique outperforms the others with an F1-score rate of 85.47%." @default.
- W4380632258 created "2023-06-15" @default.
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- W4380632258 date "2023-01-01" @default.
- W4380632258 modified "2023-09-23" @default.
- W4380632258 title "Automated Histogram Binning-Based Fuzzy K-Means Clustering for COVID-19 Chest CT Image Segmentation" @default.
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- W4380632258 doi "https://doi.org/10.1007/978-981-99-1624-5_58" @default.
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