融合三模型的深度学习法，98.83%准确率辅助新生儿脑性瘫痪早期诊断
A Hybrid Machine Learning Model for Cerebral Palsy Detection
- 用VGG19、EfficientNet、ResNet50三模型提取MRI特征，再用双向LSTM分类
- 在新生儿脑部MRI数据上达98.83%准确率，显著优于单模型（最高97.50%）
- 适合医学影像分析与儿科神经疾病筛查研究者参考
脑性瘫痪（CP）的有效治疗需在早期阶段识别患儿。本研究利用磁共振成像（MRI）技术，构建了一种混合机器学习模型以实现新生儿CP的早期诊断。首先收集脑部MRI图像数据集，并进行预处理；随后，将VGG19、EfficientNet和ResNet50三种CNN模型融合用于图像特征提取；接着，采用双向长短期记忆网络（Bi-LSTM）作为分类器判断是否存在CP。实验结果表明，该模型达到98.83%的准确率，高于VGG-19（96.79%）、EfficientNet（97.29%）及VGG-16（97.50%），相较已有预训练模型表现更优。
原文摘要 · Abstract (English)
The development of effective treatments for Cerebral Palsy (CP) can begin with the early identification of affected children while they are still in the early stages of the disorder. Pathological issues in the brain can be better diagnosed with the use of one of many medical imaging techniques. Magnetic Resonance Imaging (MRI) has revolutionized medical imaging with its unparalleled image resolution. A unique Machine Learning (ML) model that was built to identify CP disorder is presented in this paper. The model is intended to assist in the early diagnosis of CP in newborns. In this study, the brain MRI images dataset was first collected, and then the preprocessing techniques were applied to this dataset to make it ready for use in the proposed model. Following this, the proposed model was constructed by combining three CNN models, specifically VGG 19, Efficient-Net, and the ResNet50 model, to extract features from the image. Following this, a Bi-LSTM was utilized as a classifier to determine whether or not CP was present, and finally, the proposed model was employed for training and testing. The results show that the proposed model achieved an accuracy of 98.83%, which is higher than VGG-19 (96.79%), Efficient-Net (97.29%), and VGG-16 (97.50%).. When the suggested model is compared to other models that have been pre-trained in the past, the accuracy scores seem to be much higher.
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