Abstract
The article presents a comprehensive study of the application of machine learning methods for automated analysis of radiographic images of the respiratory system for the early detection of pathological changes. A method for classifying pulmonary diseases based on an ensemble of deep convolutional neural networks, including the DenseNet121, MobileNetV2, EfficientNetB0, SENet, and ShuffleNetV2 architectures, is proposed and implemented. The study included a comparative analysis of the effectiveness of various image preprocessing methods, including the use of raw black-and-white X-ray images without additional processing, the use of the CLAHE (Contrast Limited Adaptive Histogram Equalization) method in combination with color filtering, and the use of the DynamicCNN neural network denoiser for noise suppression. Experimental results showed that the ensemble approach using the soft voting strategy provides a statistically significant improvement in classification accuracy compared to individual models. The obtained results confirm the high efficiency of the proposed approach and demonstrate the potential of using ensemble deep learning models in medical diagnostics and clinical decision support tasks.