In this study, we conduct a comparative analysis of various versions of EfficientNet architectures for the identification of lung carcinoma nodules using convolutional neural networks (CNNs). Lung carcinoma remains a significant health concern globally, and early detection through medical imaging plays a crucial role in improving patient outcomes. With the emergence of EfficientNet architectures, which offer a balance between model size and accuracy through compound scaling, there is a need to evaluate their effectiveness in this specific medical imaging task. Our study explores EfficientNet models ranging from B0 to B7, where B0 represents the baseline model and B7 denotes the largest variant with increased depth, width, and resolution compared to B0, and evaluates their performance on a dataset of lung images containing carcinoma nodules. We compare metrics such as accuracy, precision, recall, and F1 score across different versions of EfficientNet to identify the most suitable architecture for lung carcinoma nodule identification. Our findings provide insights into the optimal choice of EfficientNet architecture for this critical medical imaging application, contributing to advancements in the early detection and treatment of lung carcinoma.