CNN Hyperparameter Optimization for Pulmonary Nodule Classification

• Anthony Jatobá UFAL
• Lucas Lima USP
• Lucas Amorim UFAL
• Marcelo Oliveira UFAL

DOI: https://doi.org/10.5753/sbcas.2020.11499

Abstract

Convolutional Neural Networks (CNNs) are a powerful tool to develop image-based computer-aided diagnosis systems, but as these models become more complex, manual configuration becomes unfeasible. Automatic Hyperparameter Optimization is a promising approach to model tuning, but there is no agreement on what algorithm is the right choice. In this work, we compared direct search, probabilistic search and bayesian optimization for tuning 2D and 3D CNNs for lung nodule classification. Our models achieved an AUC of 0.88, sensitivity of 87.03%, and specificity of 78.66%. Moreover, our experiments brings evidence on the weak performance of grid search, while showing that simple techniques such as random search can match probabilistic approaches.

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