<![CDATA[This study examines the effect of different optimization algorithms on the performance of the EfficientNetB2 model in classifying lung and colon histopathology images. Three commonly used optimizers AdamW, RMSprop, and Nadam were analyzed to compare their influence on convergence trends, classification accuracy, and overall learning consistency. Using a five-class dataset covering benign and malignant tissue samples, the experimental results show that all three optimizers are able to deliver reliable predictions, although with varying performance characteristics. RMSprop emerges as the most effective optimizer, achieving the highest accuracy across all evaluation stages, with 99.05% during training, 99.16% on validation, and 98.72% on testing, along with the lowest loss values. This indicates that RMSprop facilitates faster and more stable convergence compared to the other two methods. AdamW also demonstrates strong predictive performance but shows limitations when distinguishing cancer types with closely similar morphological structures. Nadam attains high accuracy in early stages yet exhibits lower initial stability than RMSprop. Overall, pairing EfficientNetB2 with RMSprop provides the most optimal configuration for this classification task. These results offer valuable insights for designing better training strategies and strengthening the effectiveness of medical imaging based computer aided diagnostic systems.]]>
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