The document discusses using a convolutional neural network (CNN) to classify chest x-ray images as having a diagnosable condition or not. It describes preprocessing the NIH chest x-ray dataset, performing a t-test to determine mean grayscale values differ between normal and abnormal images, developing and training a CNN model, and evaluating the model's performance. The CNN model achieved 63.13% validation accuracy, outperforming pre-trained ResNet50 and MobileNet models fine-tuned on the task. Future work involves further tuning and evaluating the CNN for classifying specific disease types.