Xception Model

The Xception model, a convolutional neural network architecture, is being extensively explored for its ability to efficiently and accurately classify images in diverse applications. Current research focuses on adapting and improving Xception, including modifications via neural architecture search and transfer learning from large datasets like ImageNet, to enhance performance in specific domains such as medical image analysis (e.g., Alzheimer's and COVID-19 diagnosis) and audio event detection. These advancements demonstrate Xception's versatility and potential for improving diagnostic accuracy and efficiency across various fields, particularly where labeled data is limited.