| dc.description.abstract | Skin cancer diagnosis often involves a lengthy
waiting period for biopsy results, leaving patients in
uncertainty and at risk. A patient with a suspicious lesion
may wait one to three weeks for biopsy results, impacting
their health and peace of mind. This study introduces a
deep learning system using Convolutional Neural Networks
(CNNs) to classify skin lesions as melanoma or non
melanoma. Trained on a dataset of approximately 44,000
images, the system achieves 86% accuracy, 76% precision,
and 92% recall, aiming to automate preliminary diagnoses
and reduce waiting times. The methodology includes
extensive
data
collection,
preprocessing,
model
development, and training. Future work will focus on
creating a user-friendly web application to improve
accessibility for healthcare professionals and patients.
Further research is needed to understand the model's
performance across different data subgroups and to
identify strategies for improvement. The proposed system
supports dermatologists in early skin cancer detection and
treatment, potentially transforming patient care. Its
significant impact suggests it could become a valuable tool
for both healthcare providers and patients. | en_US |
