PneumoNet: Illuminating Pneumonia Detection through CNN

Convolutional neural networks, or CNNs, strengthen the identification of pneumonia in chest X-ray images, which is an important task in medical imaging. Within the scope of this study, the capacity of CNNs to detect intricate medical imaging is used for the diagnosis of pneumonia. Beginning with a training accuracy of 98.66%, the CNN model functioned in a satisfactory manner. Epoch 2 presented obstacles, but the model persevered and improved to 98.37% validation accuracy by epoch 5. A diversified validation set of over 1,000 pictures showed the model’s generalization, with the smallest validation loss at epoch 7. These results demonstrate the model’s pneumonia classification accuracy and consistency across training epochs. The findings add to CNN integration in the medical imaging debate, notably for pneumonia diagnosis. The research acknowledges the necessity for constant refining, and the matrix of confusion and precision-recall curve analysis will give subtle insights for enhancement. This study advances the use of modern technology to improve diagnostic accuracy, which will improve healthcare practices and patient outcomes.