DL-PCMNet: Distributed Learning Enabled Parallel Convolutional Memory Network for Skin Cancer Classification with Dermatoscopic Images

Background/Objectives: Globally, one of the most dreadful and rapidly spreading illnesses is skin cancer, and it is acknowledged as a lethal form of cancer due to the abnormal growth of skin cells. Mostly, classifying and diagnosing the types of skin lesions is complex, and recognizing tumors from dermoscopic images remains challenging. The existing methods have limitations like insufficient datasets, computational complexity, class imbalance issues, and poor classification performance. Methods: This research presents a method named the Distributed Learning enabled Parallel Convolutional Memory Network (DL-PCMNet) model to effectively classify skin cancer by overcoming the existing limitations. Hence, the proposed DL-PCMNet model utilizes a distributed learning framework to provide greater flexibility during the learning process, and it increases the reliability of the model. Moreover, the model integrates the Convolutional Neural Network (CNN) and Long Short-Term Memory model (LSTM) in a parallel distribution, which enhances robustness and accuracy by capturing the information of long-term dependencies. Furthermore, the utilization of advanced preprocessing and feature extraction techniques increases the accuracy of classification. Results: The evaluation results exhibit an accuracy of 97.28%, precision of 97.30%, sensitivity of 97.17%, and specificity of 97.72% at 90% of training by using the ISIC 2019 skin lesion dataset, respectively. Conclusions: Specifically, the proposed DL-PCMNet model achieved efficient and accurate skin cancer classification compared with other existing models.