Skin cancer is classified into 2 types: melanoma and non-melanoma. Melanoma lesions are the most dangerous and aggressive, accounting for the recent increase in mortality and morbidity rates. Skin cancer detection methods typically involve several stages, including preprocessing, segmentation, feature extraction, and classification. While various transfer learning models have been proposed for skin cancer detection in recent years, they have struggled to achieve high accuracy. In this research, a hybrid deep learning model combining CNN and LSTM is introduced. The model is implemented in Python, and its performance is evaluated based on accuracy, precision, and recall. Results indicate that the proposed model outperforms existing approaches in terms of accuracy.