MCADT Model Advances Melanoma Detection
Source: Dermatology Times, January 2025
Data augmentation techniques like cropping and blurring allow AI models to overcome limited datasets, improving skin cancer detection rates.
Early detection of malignant tumors, especially melanoma, is crucial for improving treatment outcomes and survival rates. Non-melanoma skin cancers, though more common, contribute less to mortality than melanoma, which remains the most lethal skin cancer.1 Recent advancements in artificial intelligence (AI), particularly through convolutional neural networks (CNNs), have provided a significant boost in the accurate and efficient diagnosis of skin cancers, including melanoma, by analyzing medical images.
Early Detection
Observing changes in a lesion’s size, shape, or color may signal the onset of melanoma. Unfortunately, diagnosis can be delayed, particularly in individuals with darker skin, resulting in poorer outcomes. In South Africa, acral lentiginous melanoma, a subtype affecting the hands and feet, has been found to be prevalent among people with darker skin tones. This highlights the importance of improving diagnostic methods for early melanoma detection across all skin types.