Tanvir Tej
Necrotizing Fasciitis (NF) is a rare but severe bacterial infection that rapidly destroys soft tissue, leading to high morbidity and mortality rates if not promptly diagnosed and treated. Early identification is critical, as delayed treatment can result in amputation or death. Traditional diagnostic methods, such as clinical examination and laboratory testing, can sometimes be inconclusive, leading to misdiagnoses. Advances in Artificial Intelligence (AI) and deep learning provide an opportunity to improve diagnostic accuracy by analyzing digital images of affected tissues. By leveraging deep learning techniques and the hyperparameter optimization framework Optuna, researchers can develop robust models for identifying necrotizing fasciitis in medical images. Deep learning, a subset of machine learning, has demonstrated exceptional performance in image analysis and medical diagnostics. Convolutional Neural Networks (CNNs) have particularly excelled in medical imaging tasks, as they can detect intricate patterns and features that may not be immediately visible to human eyes. CNN architectures, such as ResNet, DenseNet, and EfficientNet, have been widely used for medical image classification, segmentation, and anomaly detection.
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