The　application　of　deep　learning　in　the　medical　field　has　continuously　made　huge　breakthroughs　in　recent　years.　Based　on　convolutional　neural　network　(CNN),　the　U-Net　framework　has　become　the　benchmark　of　the　medical　image　segmentation　task.　However,　this　framework　cannot　fully　learn　global　information　and　remote　semantic　information.　The　transformer　structure　has　been　demonstrated　to　capture　global　information　relatively　better　than　the　U-Net,　but　the　ability　to　learn　local　information　is　not　as　good　as　CNN.　Therefore,　we　propose　a　novel　network　referred　to　as　the　O-Net,　which　combines　the　advantages　of　CNN　and　transformer　to　fully　use　both　the　global　and　the　local　information　for　improving　medical　image　segmentation　and　classification.　In　the　encoder　part　of　our　proposed　O-Net　framework,　we　combine　the　CNN　and　the　Swin　Transformer　to　acquire　both　global　and　local　contextual　features.　In　the　decoder　part,　the　results　of　the　Swin　Transformer　and　the　CNN　blocks　are　fused　to　get　the　final　results.　We　have　evaluated　the　proposed　network　on　the　synapse　multi-organ　CT　dataset　and　the　ISIC　2017　challenge　dataset　for　the　segmentation　task.　The　classification　network　is　simultaneously　trained　by　using　the　encoder　weights　of　the　segmentation　network.　The　experimental　results　show　that　our　proposed　O-Net　achieves　superior　segmentation　performance　than　state-of-the-art　approaches,　and　the　segmentation　results　are　beneficial　for　improving　the　accuracy　of　the　classification　task.　The　codes　and　models　of　this　study　are　available　at　https://github.com/ortonwang/O-Net..