Unraveling the Effect of Demographic Factors on the Performance of Melanoma Classification

Melanoma remains the most lethal form of skin cancer, responsible for the majority of skin cancer-related fatalities despite its relatively low incidence. Melanoma can spread to different parts of the body if it is not identified and treated in its early stages. In previous work on melanoma classification, the VGG16 architecture of the CNN performed better on the cancer dataset than any other popular network. The early detection of melanoma by analyzing skin lesion images aims to enhance early diagnosis and accessibility. However, a significant challenge arises when incorporating demographic factors as biases in training data, as they can lead to disparities in model performance across different populations. These biases often stem from the underrepresentation of certain demographic groups in medical datasets, leading to lower accuracy for underserved communities. Such disparities can have serious consequences, including delayed diagnoses and inadequate treatment recommendations, further increasing healthcare inequities. Therefore, this study examines the effects of demographic factors such as age, gender, and data scalability on the early detection of melanoma. It also evaluates and compares two distinct deep learning approaches for classifying melanoma from dermoscopic images and associated patient metadata. The first experiment establishes a baseline using a VGG-16 convolutional neural network (CNN) trained via transfer learning. The second, expanded experiment introduces a novel multimodal ensemble model that synergistically combines an EfficientNetB0 CNN with a Multi-Layer Perceptron (MLP) to process both image and tabular data concurrently.