Skin cancer, a deadly form of cancer, exhibits a 23% survival rate in the USA with late diagnosis. Early detection significantly increases the survival rate to approximately 99%, facilitating timely treatment. Accurate biomedical image classification is vital in medical analysis, aiding clinicians in disease diagnosis and treatment. Deep learning (DL) techniques, including backpropagation, convolutional neural networks, and transformers, have revolutionized clinical decision-making automation. However, computational cost and hardware constraints limit the implementation of state-of-the-art DL architectures. To address these limitations, we adopted the Forward-Forward Algorithm (FFA) as a computationally efficient alternative for optimizing DL training processes for skin lesion classification. While FFA results do not surpass traditional mechanisms like backpropagation, the trade-off between computational cost and performance becomes relevant in resource-limited clinical setups. We conduct a preliminary analysis of FFA for skin lesion classification, comparing it with backpropagation and exploring their combined use during training.