Abstract
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.
Abstract (translated)
针对脑性瘫痪（CP）的有效治疗可以从早期识别患病儿童开始，特别是在疾病初期阶段。利用多种医学成像技术之一可以更准确地诊断大脑中的病理问题。磁共振成像（MRI）以其无与伦比的图像分辨率彻底革新了医学影像领域。本文介绍了一种专门用于识别脑性瘫痪的独特机器学习（ML）模型，旨在帮助早期诊断新生儿CP。 在这项研究中，首先收集了脑部MRI图像数据集，并对这些数据应用预处理技术以使其适合在提议的模型中使用。接下来，构建了一个由三种卷积神经网络（CNN）模型组合而成的建议模型，具体为VGG19、Efficient-Net和ResNet50，用于从图像中提取特征。随后，采用双向长短时记忆网络（Bi-LSTM）作为分类器来判断是否患有CP，并最终使用该提议模型进行训练与测试。 研究结果表明，所提出的模型达到了98.83%的准确率，这一成绩高于VGG-19（96.79%）、Efficient-Net（97.29%）和VGG-16（97.50%）。当将建议模型与其他先前预训练过的模型进行比较时，其准确性得分明显更高。
URL
https://arxiv.org/abs/2603.06803
