DENG ENTROPY AND INFORMATION DIMENSION FOR COVID-19 AND COMMON PNEUMONIA CLASSIFICATION

Pilar Ortiz-Vilchis, Mayra Antonio-Cruz, Mingli Lei and Aldo Ramirez-Arellano
Additional contact information
Pilar Ortiz-Vilchis: SEPI–ESM, Instituto Politécnico Nacional, Salvador Diaz Miron esq. Plan de San Luis S/N, Miguel Hidalgo, Casco de Santo Tomas, 11340 Ciudad de México, México
Mayra Antonio-Cruz: SEPI–UPIICSA, Instituto Politécnico Nacional, Avenida Té 950, Granjas México, Iztacalco, 08400 Ciudad de México, México
Mingli Lei: School of Mathematics and Statistics, Hubei Minzu University, Enshi, Hubei 445000, P. R. China4College of Computer and Information Science, Southwest University, BeiBei District, Chongqing 400715, P. R. China
Aldo Ramirez-Arellano: SEPI–UPIICSA, Instituto Politécnico Nacional, Avenida Té 950, Granjas México, Iztacalco, 08400 Ciudad de México, México

FRACTALS (fractals), 2024, vol. 32, issue 02, 1-14

Abstract: Motivated by previous authors’ work, where Shannon entropy, box covering and information dimension were applied to quantify pulmonary lesions, this paper extends such a contribution in two fashions: (i) Following the approach to quantify pulmonary lesions with Deng entropy and Deng information dimension obtained through box covering method; (ii) exploiting the Shannon and Deng lesion quantification for pulmonary illnesses classification with a bidirectional Long Short Term Memory (bLSTM). The referred pulmonary illnesses are Common Pneumonia (CP) and COVID-19. Shannon entropy and information dimension are performed here and called the Shannon sequence. Then, Deng entropy and Deng information dimension are computed for chest Computed Tomography (CT) images to obtain and combine two data sequences to quantify the pulmonary lesions. The data sequence resulting from the data combination is called the Deng sequence. Both Shannon and Deng sequences are independently used as input for the bLSTM. CT lung scans of 531 healthy subjects, 497 confirmed COVID-19 diagnoses and 516 with CP were analyzed to obtain the Shannon and Deng sequences. The results demonstrate that Deng entropy and Deng information dimension of CT images can differentiate similar lung lesions between COVID-19 and CP. Besides, a statistical analysis shows that: (a) Classification by the bLSTM is better when using the Deng sequence than the Shannon sequence; (b) Deng sequences plus bLSTM significantly outperform DenseNet-201, GoogLeNet and MobileNet-v2 in classifying COVID-19, CP and Normal CT (healthy subjects) in time and accuracy. Hence, the Deng sequence and bLSTM are fast and accurate tools for helping in diagnosing CP and COVID-19.

Keywords: Deng Entropy; Deng Information Dimension; Long Short Term Memory; Lung Lesions; COVID-19 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X24500336
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:32:y:2024:i:02:n:s0218348x24500336

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X24500336

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().