A custom CNN is reported to match ResNet50 accuracy on HAM10000 at 692K parameters and 30M FLOPs, but the paper's own layer table implies 25.8M parameters and hundreds of millions of FLOPs.
Anomaly detection in biomedical data and image using various shallow and deep learning algorithms,
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Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off
A custom CNN is reported to match ResNet50 accuracy on HAM10000 at 692K parameters and 30M FLOPs, but the paper's own layer table implies 25.8M parameters and hundreds of millions of FLOPs.