A Machine Learning Model for Cerebral Palsy Disorder Detection in Integration with Hybrid Optimization
Keywords:
Cerebral Palsy, Machine Learning (ML), Deep Learning (DL), MRI, Disorders.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. 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 Magnetic Resonance Imaging (MRI) images dataset was collected followed by preprocessing. 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. A Bi-LSTM was utilized as a classifier to determine the presence of CP and finally, the proposed model was used for training and testing. The outcomes established that the suggested model accomplished an accuracy of 98.83%, which is more than the accuracy accomplished by 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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