EfficientNet-driven compound scaling for performance enhancement of CNNs in multimodal cancer imaging
Keywords:
Convolutional neural networks, Deep learning, Magnetic resonance imaging, Computed tomography, Brain tumor detection, Lung cancer detectionAbstract
Recent developments in deep learning-based medical image analysis have paved the way forward in improving the accuracy of cancer detection. In this study, we focused on enhancing the performance of different variants of existing Convolutional Neural Network (CNN) architectures by leveraging the compound scaling feature of EfficientNet. The EfficientNet architecture is known to scale the network width, resolution, and depth using a specific scaling factor. We integrated the compound scaling factor of EfficientNet-B2 to scale the network width and resolution of conventional models while preserving the default depth of all architectures. The proposed approach is evaluated on two distinct medical imaging tasks: lung cancer detection using Computed Tomography (CT) scans and brain tumor classification using Magnetic Resonance Imaging (MRI). Multiple CNN architectures, including different variants of VGG, ResNet, and DenseNet, are assessed in both baseline and scaled configurations. On the CT dataset, the proposed method achieves an accuracy improvement of up to 8.30%, while on the MRI dataset, improvements reach 12.95%. These improvements suggest that compound scaling is a suitable strategy to enhance the utilization of the existing conventional CNN architecture instead of replacing it.
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The data supporting this study are available from the corresponding author upon reasonable request.
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