Enhanced COVID-19 X-ray image preprocessing schema using type-2 neutrosophic set

Mohamed Abdel-Basset, Nihal N. Mostafa, Karam M. Sallam, Ibrahim Elgendi, Kumudu Munasinghe

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this study, we introduce a new medical image enhancement approach depending on a type-2 neutrosophic set (T2NS) and α-mean and β- enhancement operations. This new approach obtains a good enhancement result by defining the uncertainties within the image in a six-degree membership. To show the real case study of this proposed technique, a novel enhancement approach for COVID-19 in X-ray is introduced. The X-ray image suffers from poor contrast and inconsistencies in its gray levels. The proposed method tackles this issue by obtaining a neutrosophic domain for gray level images depending on six membership functions. Through enhancement operations, T2NS entropy is obtained to evaluate the change in the gray level of X-ray images. The proposed approach can improve chest X-ray images by reducing the entropy values to minimize the uncertainty within the image. An image de-neutrosophication operation is obtained on the enhanced images to convert them from the neutrosophic set (NS) domain to the grayscale image. Finally, output images are compared with the enhanced images achieved under a single-valued neutrosophic set (SVNS) domain.

Original languageEnglish
Article number108948
Pages (from-to)1-13
Number of pages13
JournalApplied Soft Computing
Publication statusPublished - Jul 2022


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