  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.
