Abstract
This paper optimizes a neural network classifier for detecting malignant melanoma, a type of skin cancer. A popular metaheuristic algorithm, Particle Swarm Optimization (PSO) is used for finding the optimal number of neuron in the hidden layers of multi-layer neural network (MLP). Using a total of 1875 color, texture and shape features extracted from 170 color images from MED-NODE dataset an accuracy of 85.9% is achieved with threefold cross-validation using two-layer neural network, which is 4.9% higher accuracy rate than previously reported result with the same dataset.
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Mukherjee, S., Adhikari, A., Roy, M. (2019). Malignant Melanoma Detection Using Multi Layer Perceptron with Optimized Network Parameter Selection by PSO. In: Mandal, J., Sinha, D., Bandopadhyay, J. (eds) Contemporary Advances in Innovative and Applicable Information Technology. Advances in Intelligent Systems and Computing, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-13-1540-4_11
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DOI: https://doi.org/10.1007/978-981-13-1540-4_11
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