  Skin cancer detection is challenging since different types of skin lesions
share high similarities. This paper proposes a computer-based deep learning
approach that will accurately identify different kinds of skin lesions. Deep
learning approaches can detect skin cancer very accurately since the models
learn each pixel of an image. Sometimes humans can get confused by the
similarities of the skin lesions, which we can minimize by involving the
machine. However, not all deep learning approaches can give better predictions.
Some deep learning models have limitations, leading the model to a
false-positive result. We have introduced several deep learning models to
classify skin lesions to distinguish skin cancer from different types of skin
lesions. Before classifying the skin lesions, data preprocessing and data
augmentation methods are used. Finally, a Convolutional Neural Network (CNN)
model and six transfer learning models such as Resnet-50, VGG-16, Densenet,
Mobilenet, Inceptionv3, and Xception are applied to the publically available
benchmark HAM10000 dataset to classify seven classes of skin lesions and to
conduct a comparative analysis. The models will detect skin cancer by
differentiating the cancerous cell from the non-cancerous ones. The models
performance is measured using performance metrics such as precision, recall, f1
score, and accuracy. We receive accuracy of 90, 88, 88, 87, 82, and 77 percent
for inceptionv3, Xception, Densenet, Mobilenet, Resnet, CNN, and VGG16,
respectively. Furthermore, we develop five different stacking models such as
inceptionv3-inceptionv3, Densenet-mobilenet, inceptionv3-Xception,
Resnet50-Vgg16, and stack-six for classifying the skin lesions and found that
the stacking models perform poorly. We achieve the highest accuracy of 78
percent among all the stacking models.
