In　recent　years,　with　extensive　application　in　image　retrieval　and　other　tasks,　a　convolutional　neural　network　(CNN)　has　achieved　outstanding　performance.　In　this　paper,　a　new　content-based　medical　image　retrieval　(CBMIR)　framework　using　CNN　and　hash　coding　is　proposed.　The　new　framework　adopts　a　Siamese　network　in　which　pairs　of　images　are　used　as　inputs,　and　a　model　is　learned　to　make　images　belonging　to　the　same　class　have　similar　features　by　using　weight　sharing　and　a　contrastive　loss　function.　In　each　branch　of　the　network,　CNN　is　adapted　to　extract　features,　followed　by　hash　mapping,　which　is　used　to　reduce　the　dimensionality　of　feature　vectors.　In　the　training　process,　a　new　loss　function　is　designed　to　make　the　feature　vectors　more　distinguishable,　and　a　regularization　term　is　added　to　encourage　the　real　value　outputs　to　approximate　the　desired　binary　values.　In　the　retrieval　phase,　the　compact　binary　hash　code　of　the　query　image　is　achieved　from　the　trained　network　and　is　subsequently　compared　with　the　hash　codes　of　the　database　images.　We　experimented　on　two　medical　image　datasets:　the　cancer　imaging　archive-computed　tomography　(TCIA-CT)　and　the　vision　and　image　analysis　group/international　early　lung　cancer　action　program　(VIA/I-ELCAP).　The　results　indicate　that　our　method　is　superior　to　existing　hash　methods　and　CNN　methods.　Compared　with　the　traditional　hashing　method,　feature　extraction　based　on　CNN　has　advantages.　The　proposed　algorithm　combining　a　Siamese　network　with　the　hash　method　is　superior　to　the　classical　CNN-based　methods.　The　application　of　a　new　loss　function　can　effectively　improve　retrieval　accuracy.