CSE, SOET, The NorthCap University, Gurugram, India.
J Med Eng Technol. 2023 Jul;47(5):288-297. doi: 10.1080/03091902.2024.2327459. Epub 2024 Mar 22.
Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately apparent to the naked eye. Burns can have catastrophic and incapacitating effects and if they are not treated on time can cause scarring, organ failure, and even death. Burns are a prominent cause of considerable morbidity, but for a variety of reasons, traditional clinical approaches may struggle to effectively predict the severity of burn wounds at an early stage. Since computer-aided diagnosis is growing in popularity, our proposed study tackles the gap in artificial intelligence research, where machine learning has received a lot of attention but transfer learning has received less attention. In this paper, we describe a method that makes use of transfer learning to improve the performance of ML models, showcasing its usefulness in diverse applications. The transfer learning approach estimates the severity of skin burn damage using the image data of skin burns and uses the results to improve future methods. The DL technique consists of a basic CNN and seven distinct transfer learning model types. The photos are separated into those displaying first, second, and third-degree burns as well as those showing healthy skin using a fully connected feed-forward neural network. The results demonstrate that the accuracy of 93.87% for the basic CNN model which is significantly lower, with the VGG-16 model achieving the greatest accuracy at 97.43% and being followed by the DenseNet121 model at 96.66%. The proposed approach based on CNN and transfer learning techniques are tested on datasets from Kaggle 2022 and Maharashtra Institute of Technology open-school medical repository datasets that are clubbed together. The suggested CNN-based approach can assist healthcare professionals in promptly and precisely assessing burn damage, resulting in appropriate therapies and greatly minimising the detrimental effects of burn injuries.
目视检查是评估烧伤的典型方法，由于全球烧伤发生率的上升，目视检查可能不足以发现皮肤烧伤，因为烧伤的严重程度可能有所不同，有些烧伤可能肉眼无法立即察觉。烧伤可能会造成灾难性和使人丧失能力的影响，如果不及时治疗，可能会导致疤痕、器官衰竭，甚至死亡。烧伤是导致发病率相当高的主要原因，但由于各种原因，传统的临床方法可能难以在早期有效预测烧伤伤口的严重程度。由于计算机辅助诊断越来越受欢迎，我们提出的研究解决了人工智能研究中的一个空白，机器学习受到了很多关注，但迁移学习受到的关注较少。在本文中，我们描述了一种利用迁移学习来提高机器学习模型性能的方法，展示了它在各种应用中的有用性。迁移学习方法使用皮肤烧伤的图像数据来估计皮肤烧伤的严重程度，并利用结果来改进未来的方法。DL 技术包括一个基本的 CNN 和七种不同的迁移学习模型类型。这些照片通过全连接前馈神经网络分为显示一度、二度和三度烧伤的照片以及显示健康皮肤的照片。结果表明，基本 CNN 模型的准确率为 93.87%，明显较低，VGG-16 模型的准确率最高，为 97.43%，其次是 DenseNet121 模型，准确率为 96.66%。基于 CNN 和迁移学习技术的建议方法在 Kaggle 2022 数据集和 Maharashtra Institute of Technology 开放学校医疗存储库数据集上进行了测试，这两个数据集被组合在一起。基于 CNN 的建议方法可以帮助医疗保健专业人员快速准确地评估烧伤损伤，从而实施适当的治疗，最大限度地减少烧伤损伤的不利影响。
