The accurate detection of abnormalities in medical imaging is crucial for timely diagnosis; however, the performance of deep learning models in this task depends significantly on the choice of optimization techniques. This chapter presents a detailed empirical study comparing the effectiveness of modern optimization algorithms, such as Adam, SGD with momentum, RMSprop, and adaptive optimizers, for improving lesion, tumor, or anomaly detection in medical images. We evaluated these optimizers on benchmark datasets, using key metrics such as detection accuracy, false positive rates, and model convergence speed. Our results demonstrate that adaptive optimization methods, particularly when combined with dynamic learning rate adjustments, achieve superior detection performance compared with traditional approaches. This study provides practical insights for researchers and clinicians in selecting optimization strategies to enhance the reliability of artificial intelligence (AI)-assisted medical image detection systems.