Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm

The global spread of monkeypox across over 40 countries is a major health challenge. Its symptoms resemble those of chickenpox and measles, complicating early diagnosis. When PCR tests are unavailable, computational lesion detection offers a promising option. This research presents a non-invasive diagnostic method using the Sparrow Search Algorithm (SpaSA). SpaSA is applied to improve accuracy in medical imaging tasks. Deep learning, especially CNNs, effectively addresses challenges in medical imaging. By combining CNNs with optimization, we analyze large imaging datasets. This study evaluates several pre-trained models on two datasets: "Monkeypox 2022" and "Images of Monkeypox." The method classifies patients as either "normal" or "pox" (including monkeypox, chickenpox, smallpox, cowpox, and measles). The novelty lies in using SpaSA to optimize pre-trained CNNs' performance. The Xception model achieves 97.66% accuracy without optimization. SpaSA enhances VGG19's performance, reaching 99.87% accuracy. With SpaSA, VGG19 reached 99.87% accuracy in 120 seconds. SpaSA had the fastest time (120s) and lowest failure rate (5%). It outperformed Grid Search, Random Search, Bayesian Optimization, WOA, GA, PSO, and ACO. This shows mathematical optimization's impact on medical imaging. It sets new standards in healthcare diagnostics using SpaSA and CNNs. This research shows the significant role of optimization in improving medical imaging. It sets new standards for healthcare diagnostics using SpaSA and CNNs.