Deep Learning Algorithms for Pneumonia: A Comparative Approach to Classification and Segmentation

The study compares the outcomes of numerous modern CNN architectures, such as ResNet-50, VGG-16, and DenseNet-121, with conventional machine learning classifiers in order to assess each architecture’s performance. Recent developments in deep learning have shown that medical image analysis, particularly the diagnosis of pneumonia, can be done with great accuracy. This work investigates different deep learning techniques for lung X-ray-based pneumonia classification. The study emphasizes the use of convolutional neural networks (CNNs) to analyze radiographic images and distinguish between pneumonia-affected lung tissue and normal lung tissue.. This underscores the significant potential of deep learning algorithms to enhance the accuracy and efficiency of pneumonia diagnosis, aiding medical professionals in making informed patient care decisions. The results indicate that deep learning methods surpass traditional machine learning techniques in classifying pneumonia, achieving approximately $\mathbf{9 5 \%}$ accuracy across all examined CNN models. These deep learning models are trained on an extensive dataset of annotated chest X-rays, which allows them to recognize intricate patterns and characteristics linked to pneumonia. Considering the substantial influence pneumonia has on the world’s health, prompt and correct diagnosis is essential to successful management and treatment.