UreteroPelvic　Junction　Obstruction　(UPJO)　is　a　common　hydronephrosis　disease　in　children　that　can　result　in　an　even　progressive　loss　of　renal　function.　Ultrasonography　is　an　economical,　radiationless,　noninvasive,　and　high　noise　preliminary　diagnostic　step　for　UPJO.　Artificial　intelligence　has　been　widely　applied　to　medical　fields　and　can　greatly　assist　doctors＇　diagnostic　abilities.　The　demand　for　a　highly　secure　network　environment　in　transferring　electronic　medical　data　online,　therefore,　has　led　to　the　development　of　blockchain　technology.　In　this　study,　we　built　and　tested　a　framework　that　integrates　a　deep　learning　diagnosis　model　with　blockchain　technology.　Our　diagnosis　model　is　a　combination　of　an　attention-based　pyramid　semantic　segmentation　network　and　a　discrete　wavelet　transformation-processed　residual　classification　network.　We　also　compared　the　performance　between　benchmark　models　and　our　models.　Our　diagnosis　model　outperformed　benchmarks　on　the　segmentation　task　and　classification　task　with　MIoU　=　87.93,　MPA　=　93.52,　and　accuracy　=　91.77%.　For　the　blockchain　system,　we　applied　the　InterPlanetary　File　System　protocol　to　build　a　secure　and　private　sharing　environment.　This　framework　can　automatically　grade　the　severity　of　UPJO　using　ultrasound　images,　guarantee　secure　medical　data　sharing,　assist　in　doctors＇　diagnostic　ability,　relieve　patients＇　burden,　and　provide　technical　support　for　future　federated　learning　and　linkage　of　the　Internet　of　Medical　Things　(IoMT).