Skin cancer is a widespread malignant disease that is present in the skin tissue. Skin cancer is a life-threatening disease, and preventing it in the early stage is essential to save the life of patients. Detection of skin cancer early can cure the disease and reduce mortality rates by early detection with accurate diagnosis, and it is the key objective of this research. Recent studies focused on machine learning (ML) and deep learning (DL) algorithms to diagnose skin cancer using dermoscopic images. This study employs a computer-aided detection mechanism of skin cancer using the LSTM model for accurate classification. The LSTM is improved based on the Modified Electromagnetic Field Optimization Algorithm (MEFOA) to adjust the parameters and increase the accuracy level. The LSTM-MEFOA model uses the MNIST dataset with 10,000 images for classification. The proposed work attained an accuracy of 98.99%, precision of 97.87%, recall of 98.65%, and F1-score of 94.54%. The experimental outcome proves that LSTM-MEFOA outperforms well and achieves a high-accuracy level evaluated to the present state-of-the-art techniques.