COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

• URL: http://arxiv.org/abs/2312.12876v1
• Date: Wed, 20 Dec 2023 09:39:53 GMT
• Title: COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification
• Authors: Vida Esmaeili and Mahmood Mohassel Feghhi and Seyed Omid Shahdi
• Abstract summary: We propose a novel method, named as the ULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy.
• Score: 3.683202928838613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The contagious and pandemic COVID-19 disease is currently considered as the main health concern and posed widespread panic across human-beings. It affects the human respiratory tract and lungs intensely. So that it has imposed significant threats for premature death. Although, its early diagnosis can play a vital role in revival phase, the radiography tests with the manual intervention are a time-consuming process. Time is also limited for such manual inspecting of numerous patients in the hospitals. Thus, the necessity of automatic diagnosis on the chest X-ray or the CT images with a high efficient performance is urgent. Toward this end, we propose a novel method, named as the ULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. In fact, this method includes Uniform Local Binary Pattern (ULBP), Gabor Filter (GF), and ResNet51. According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy.

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