  Skin cancer, a deadly form of cancer, exhibits a 23\% survival rate in the
USA with late diagnosis. Early detection can significantly increase the
survival rate, and facilitate timely treatment. Accurate biomedical image
classification is vital in medical analysis, aiding clinicians in disease
diagnosis and treatment. Deep learning (DL) techniques, such as 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. In this work, we explore a
new type of neural network that does not need backpropagation (BP), namely the
Forward-Forward Algorithm (FFA), for skin lesion classification. While FFA is
claimed to use very low-power analog hardware, BP still tends to be superior in
terms of classification accuracy. In addition, our experimental results suggest
that the combination of FFA and BP can be a better alternative to achieve a
more accurate prediction.
