Skin cancers have become one of the most common malignancies globally
and a healthcare burden that creates a diagnosis difficulty due to their wide nature.
Early and accurate detection is crucial for patient outcomes, yet conventional methods
largely depend on subjective clinical judgment. The recent progress of artificial
intelligence (AI), especially deep learning (DL) and transfer learning (TL), gives
potential tools for improving the ability to detect skin cancer. DL Algorithms such as
CNN and TL for a better diagnosis by using pre-trained models are discussed in this
chapter, tackling several prominent issues like small annotated datasets and
heterogeneity in dermoscopic images. TL addresses common bottlenecks in medical
image analysis, such as limited availability of annotated datasets and high required
annotation cost, by permitting a model to be trained using smaller-size domain-specific
datasets, therefore offering a much more profitable approach. We outline the standing
of AI-assisted systems in clinical workflows, examine the performance of the DL
model in dermatological skin cancer detection, and discuss the possibility of this
technology in enhancing human dermatological competence. This chapter highlights
future research directions such as hybrid models, multimodal data integration, and AI's
ethical arrangement in healthcare. Our work focuses on utilizing DL and TL to improve
the early detection of skin cancer, which will help to lay the groundwork for more
personalized and effective treatment options.
Keywords: Convolutional Neural Networks (CNN), Dermatology AI, Deep Learning, Image Classification, Medical Imaging, Skin Cancer Detection, Transfer Learning.
