Computer-aided　image　classification　has　achieved　start-of-the-art　performance　since　Convolutional　Neural　Network　structures　were　employed.　Classical　neural　networks　such　as　AlexNet　and　VGG-Net　inspired　several　rules　of　designing　network　models.　Besides　benchmark　datasets　such　as　MNIST,　CIFAR　and　ImageNet,　classification　performance　of　medical　images　such　as　chromosome　karyotyping　images　also　improved　via　Convolutional　Neural　Network.　However,　there　are　few　studies　on　generalization　among　different　datasets.　In　this　paper,　we　designed　a　neural　network　with　nine　layers,　and　achieved　classification　accuracy　of　0.984,　0.816　and　0.921　on　the　dataset　of　MNIST,　CIFAR　and　chromosome　karyotype　images.　We　also　visualized　the　output　of　several　layers　of　the　model　and　explained　that　smooth　output　between　neural　network　layers　may　induce　lower　accuracy　on　classification.　©　2020　IOP　Publishing　Ltd.　All　rights　reserved.