Detection of down syndrome from facial expressions using transfer deep learning methods

This study was conducted to detect diseases using artificial intelligence and deep learning techniques. These technologies are employed to ensure that diseases are diagnosed quickly, accurately, and efficiently. Deep learning algorithms, especially when trained on large datasets, create powerful models capable of detecting complex and subtle details. The dataset used in the study was obtained from open-source sites. The focus of the research was on the detection of genetic disorders such as Down syndrome using facial images and deep convolutional neural networks (CNNs). Popular CNN models like ResNet50, ResNet101, ResNet152, and MobileNet were used to perform classification tasks aimed at speeding up the disease detection process and increasing its accuracy. The findings reveal that ResNet50 and ResNet101 models have higher accuracy rates compared to other models. The 99% accuracy rate of these two models demonstrates their high effectiveness in detecting Down syndrome. These high accuracy rates indicate that the models can accurately identify distinctive features in facial images and work with great reliability in disease diagnosis. These models can detect prominent features of diseases like genetic disorders and perform automatic classification. The results suggest that such artificial intelligence and deep learning techniques can play a significant role in disease detection. Therefore, artificial intelligence technologies are considered a powerful tool to enhance the quality of disease detection and healthcare services overall. The advantages offered by artificial intelligence have the potential to bring revolutionary changes to the healthcare sector.