The rising global prevalence of skin conditions, some of which can escalate to life-threatening stages if not timely diagnosed and treated, presents a significant healthcare challenge. This issue is particularly acute in remote areas where limited access to healthcare often results in delayed treatment, allowing skin diseases to advance to more critical stages. One of the primary challenges in diagnosing skin diseases is their low inter-class variations, as many exhibit similar visual characteristics, making accurate classification challenging. This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. This approach mimics the diagnostic process employed by medical professionals. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. This component plays a crucial role in refining visual details and enhancing feature extraction, leading to improved differentiation between classes and, consequently, elevating the overall effectiveness of the model. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures. The results of these experiments not only demonstrate the effectiveness of the proposed method but also its potential applicability under-resourced healthcare environments.

翻译：全球范围内皮肤疾病的患病率持续上升，若未能及时诊断和治疗，部分病症可能发展为危及生命的阶段，这对医疗体系构成了重大挑战。这一问题在偏远地区尤为突出，因医疗资源可及性有限，常导致治疗延误，使皮肤疾病发展至更严重阶段。皮肤疾病诊断的主要挑战之一在于其类间差异较小——许多病症呈现相似视觉特征，使得准确分类困难重重。本研究提出一种创新的多模态皮肤病变分类方法，将智能手机拍摄的图像与必要的临床和人口统计信息相结合，该方法模拟了医疗专业人员采用的诊断流程。该方法的独特之处在于集成了超分辨率图像预测的辅助任务。这一组件在细化视觉细节和增强特征提取方面发挥关键作用，从而提升不同类别间的区分能力，最终提高模型的整体效能。实验采用PAD-UFES20数据集，基于多种深度学习架构开展评估。实验结果不仅证明了所提方法的有效性，还展示了其在医疗资源匮乏环境中的潜在应用价值。