Göğüs röntgeni görüntülerinde çocuk pnömonisinin otomatik olarak tespitinde derin öğrenme ve makine öğrenme yöntemlerinin etkinliğinin değerlendirilmesi

Accurate identification and classification of images of patients with pneumonia is vital for effective diagnosis and treatment. Advanced learning techniques such as CNN LSTM (Convolutional Neural Network Long Short-Term Memory) have proven 97% accuracy on this task. A labeled dataset of 5,856 images of pediatric pneumonia patients from Kaggle was used to evaluate these models. The dataset was divided into training (70%), testing (15%), and validation (15%) sets. It is worth noting that the ResNet 50 -CNN model and the CNN-ExtraTrees model achieved an accuracy rate of 98% and 94%, respectively. These models provide resources for clinicians and radiologists in their decision-making processes. Extracting features from images plays a role in pattern prediction using the optimizer as part of the modeling process. To improve performance, modifications were made to the RMSprop parameters. Model performance evaluation included metrics such as recall, precision, F1 score, and overall accuracy evaluation. These measures provide insight into the effectiveness of models while also serving as reference points for investigations. The exceptional accuracy rates achieved by these models confirm their ability to accurately classify patients with pneumonia, which has implications for improving patient outcomes. The evaluation criteria used in this research provide an examination of the advantages and limitations of models that highlights the importance of incorporating these metrics to ensure accurate performance in scenarios. Keywords: Medical Image, Pediatric Pneumonia, Resnet50-CNN, CNN-Extratrees, Convolutional Neural Network, Long Short-Term Memory