Evaluating Hybrid Deep Learning Approach

Some effective parameters were used to evaluate the performance of the proposed hybrid deep learning approach. These parameters are given by the following equations^{60 (link)}:

Accuracy = \frac{T p + T n}{T p + F p + T n + F n} \times 100 %,

Precision = \frac{Tp}{F p + T p} \times 100 %,

Specificity = \frac{Tn}{T n + F p} \times 100 %,

Sensitivity = \frac{Tp}{T p + F n} \times 100 %,

F_{1} -score = \frac{2 T p}{2 T p + F p + F n},

Matthew's correlation coefficient (MCC) = \frac{(T p \times T n) - (F n \times F p)}{\sqrt{(T p + F p) (F p + T n) (T n + F n) (F n + T p)}},

where Fn and Tn denote false-negative and true-negative values, and Fp and Tp denote the false-positive and true-positive values, respectively.

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Hammad M.S., Ghoneim V.F., Mabrouk M.S, & Al-atabany W.I. (2023). A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques. Scientific Reports, 13, 4003.

Publication 2023

Hybrid Sensitivity Tptp

Corresponding Organization : Helwan University

Other organizations : Misr University for Science and Technology

Top 5 similar protocols

Variable analysis

independent variables

Hybrid deep learning approach

dependent variables

Accuracy
Precision
Specificity
Sensitivity
F1-score
Matthew's correlation coefficient (MCC)

control variables

Not explicitly mentioned

Annotations

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