A Hybrid Machine Learning Model for Cerebral Palsy Detection

TL;DR

This paper presents a novel hybrid machine learning model combining CNNs and Bi-LSTM for early detection of Cerebral Palsy using MRI images, achieving high accuracy and outperforming existing models.

Contribution

The study introduces a combined CNN and Bi-LSTM model for CP detection, integrating three CNN architectures for feature extraction and a Bi-LSTM classifier, with superior accuracy.

Findings

Achieved 98.83% accuracy in CP detection.

Outperformed individual CNN models in accuracy.

Demonstrated effectiveness of hybrid CNN-Bi-LSTM approach.

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.