 The proposed model features an autoencoder-based network for scale adaptation and a CNN classification network. It has been shown to achieve higher diagnostic accuracy than other models, with accuracy, sensitivity and specificity reaching 96.2%, 96.2% and 99%, respectively. This article was authored by Naveen Nasser-Elden, Ahmed Nagler, Mohamed El-Sharkoui and others.