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.

翻译：皮肤癌是一种致命的癌症，在美国晚期诊断的生存率仅为23%。早期检测能显著提高生存率，并促进及时治疗。准确的生物医学图像分类在医学分析中至关重要，有助于临床医生进行疾病诊断和治疗。深度学习方法（如卷积神经网络和Transformer）已彻底改变了临床决策自动化的进程。然而，计算成本和硬件限制制约了最先进深度学习架构的实现。本研究探索了一种无需反向传播的新型神经网络——前向-前向算法（Forward-Forward Algorithm, FFA），用于皮肤病变分类。尽管FFA声称使用极低功耗的模拟硬件，但在分类准确率方面，BP仍然更具优势。此外，我们的实验结果表明，将FFA与BP相结合可能是实现更准确预测的更优方案。