Abstract
Skin cancer is defined as unregulated cell proliferation due to irreversible DNA damage. Melanoma is one kind of skin cancer generated by melanocytes, which could result in serious health issues. Early detection of this skin cancer based on image processing assist therapists in its treatment. Computational pathology has the unique capability of spatially dissecting specific interfaces on digitized histology images. The main objective of this research is to classify skin cancer using a deep learning model. This work proposes a hybrid context-aware convolutional neural network with recurrent neural network (CA-CNN-RNN) based on skin cancer histology images. This model encodes the local representation of the histological images into high-dimensional features and hence aggregates the features by considering their spatial configuration for performing final classification. The dataset for this work is made up of H&E-stained images from the database of The Cancer Genome Atlas. Using the hybrid CA-CNN-RNN model, the experiment on histological image of melanoma disease was performed and validated with several classification models like DarkNet-53, VGG-19, ResNet50, and Inception. The data set is utilized to evaluate the model in order to provide results, which are analyzed using parameters like as accuracy, precision, recall, and F1-score. The experimental analysis reveals that the CA-CNN-RNN performed better with the DarkNet-53 model. The proposed model achieved 97.14 accuracy, 96.49 precision, 98.21 recall, and 96.50 f-score.