Table 2 Performance comparison on lung cancer using various bio-inspired algorithms.
From: Bio inspired optimization techniques for disease detection in deep learning systems
Algorithm | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) | Specificity (%) | Sensitivity (%) |
|---|---|---|---|---|---|---|
Particle Swarm Optimization (PSO)6 | 88.0 | 90.0 | 85.3 | 87.9 | 91.4 | 84.6 |
Artificial Bee Colony (ABC)12 | 85.6 | 88.9 | 81.6 | 85.6 | 89.4 | 86.6 |
Cuckoo Search (CS)34 | 84.6 | 86.4 | 81.6 | 84.9 | 88.9 | 87.6 |
Grey Wolf Optimizer (GWO)20 | 91.5 | 92.4 | 88.6 | 91.5 | 93.4 | 89.6 |
Dragonfly Algorithm (DA)49 | 87.9 | 89.6 | 84.6 | 87.6 | 91.4 | 87.1 |
Genetic Algorithms (GA)14 | 92.6 | 94.7 | 91.6 | 93.5 | 94.6 | 86.5 |
Ant Colony Optimization (ACO)60 | 86.9 | 94.6 | 85.6 | 88.6 | 88.4 | 90.3 |
Firefly Algorithm (FA)56 | 84.6 | 89.6 | 82.4 | 84.6 | 89.6 | 91.0 |
Bat Algorithm (BA)23 | 87.9 | 91.6 | 86.5 | 88.9 | 94.6 | 89.3 |
Whale Optimization Algorithm (WOA)27 | 86.5 | 91.4 | 86.2 | 88.6 | 90.4 | 88.1 |
