A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.
Deep Learning Approach for Early Stage Lung Cancer Detection
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abstract
Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode achieves high accuracy. In addition, it can be a beneficial tool to support radiologists' decisions in predicting and detecting lung cancer and its stage.
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Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging
A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.