الخلاصة:
Skin cancer, one of the fastest-growing cancer types globally, includes both
benign and malignant forms. Recent advances in artificial intelligence have
enabled the early detection of various diseases with high accuracy, accelerating
treatment processes. Convolutional neural networks (CNNs) are widely regarded
as powerful tools in artificial intelligence, particularly for deep learning (DL)
applications in medical image analysis, including skin cancer detection. This
study evaluates a range of pre-trained CNN models namely, VGG16, ResNet50,
MobileNetV2, DenseNet201, VGG19, Xception, InceptionV3, and EfficientNetB0
for their effectiveness in classifying skin cancer images from the ISIC Archive
dataset
