Abstract:
As skin cancer remains the most frequent type of cancer worldwide, patient
outcomes need accurate diagnostic techniques. In the following paper, an in-depth
comparison of advanced segmentations with trusted deep learning models for the
classification of skin cancer images is presented. For the preparation of an original
segmented dataset, the original dataset was initially preprocessed and then
segmented into K-means clustering. It was subjected to the application of deep
learning architectures, including existing pre-trained models such as
DenseNet201, ResNet50, ConvNeXt_Base, EfficientNetV2-L, InceptionV3,
Xception, and Swin Transformer-B, and an innovatively developed hybrid CNN–
ViT model, in the subsequent steps. CycleGAN was employed for efficient
augmentation of the dataset in order to solve issues related to class imbalance,
which is typical for medical data. The same deep learning models, including
DenseNet201, ResNet50, ConvNeXt_Base, Swin Transformer-B, and the proposed
hybrid CNN–ViT model, were employed for the development of U-Net++ in order
to construct the complex segmentation method. With 90% accuracy, the proposed
hybrid CNN–ViT model performed best among them. Various quantitative
assessments, including confusion matrix, loss curves, ROC curves, and accuracy,
indicate the potential for the proposed model if combined with CycleGAN-based
augmentation. Comparative results indicated that while U-Net++ segmented
dataset models achieved up to 88.2% accuracy, 89% accuracy was achieved in Kmeans clustering segmentation. Valuable insights into the relative superiority of
K-means compared with U-Net++ segmentation methods are provided through the
comparison work, in addition to providing important tips for the selection of the
best preprocessing methods in clinical scenarios. The conclusions in the paper have
enormous possibilities for enhancing computerized skin cancer detection, and it has
the prospect of contributing towards enhanced survival rates in patients, as well
as early detection rates.
