Model hybridization & learning rate annealing for skin cancer detection.

The increasing frequency of skin tumour across the globe and their timely diagnosis is one of the most promising research directions in the healthcare domain. The most important cause behind the skin cancer mortalities is delayed detection. Early detection followed by adequate treatment may enhance the chances of human survival to a great extent. However, extracting the features from the tumour images for the possible detection of skin cancer is not a trivial task. Numerous deep learning models are extensively employed for the efficient features' extraction for skin cancer detection but literature demonstrate further scope of improvements in various performance measures. In this paper, we propose a hybrid deep Convolutional Neural Network architecture inspired from pretrained architectures for the skin cancer detection by incorporating three major heuristics viz. usage of multiple smaller sized convolutional filters instead of using a single larger filter homogeneously and consistently across the entire model, utilization of skip or residual connections to mitigate the vanishing gradient problem in the deeper model, and learning rate annealing by introducing cyclic learning rate. Experimental results performed on HAM10000 dataset observed an improvement in various performance measures and faster model convergence to a significant extent in comparison with the state-of-the-arts. [ABSTRACT FROM AUTHOR]