Diffusion　optical　tomography　(DOT)　is　an　emerging　optical　imaging　modality　with　the　advantages　of　being　low-cost,　non-invasive　and　non-damage-free,　it　has　demonstrated　great　potential　in　differentiating　benign　and　malignant　breast　tumors.　However,　due　to　the　strong　scattering　of　biological　tissues　and　the　limited　number　of　boundary　measurements,　the　DOT　image　reconstruction　is　ill-posed　and　ill-conditioned.　Conventional　DOT　image　reconstruction　algorithms　such　as　Tikhonov　regularization,　are　easy　to　implement.　Nevertheless,　images　with　poor　quality　were　usually　achieved.　In　this　paper,　to　improve　the　image　quality　of　DOT,　we　develop　a　post-processing　reconstruction　algorithm　based　on　deep　learning.　It　has　a　type　of　U-net　architecture,　but　with　the　low-quality　image　obtained　with　Tikhonov　regularization　and　the　acquired　multi-wavelength　optical　signals　as　the　input.　Meanwhile,　the　output　of　the　network　is　images　of　chromophores,　including　HbO,　Hb　and　water.　To　train　the　proposed　algorithm,　we　construct　a　dataset　with　15,　000　images　for　training,　6,　000　for　validation,　and　3,　900　for　testing.　Our　results　demonstrate　that　it　has　achieved　good　reconstruction　results　for　the　phantoms　with　single　or　double　inclusions　in　terms　of　PSNR　and　SSIM.　Compared　to　the　Tikhonov　regularization,　the　average　PSNR　of　HbO,　Hb　and　water　are　improved　by　55%,　74%　and　71%,　respectively,　and　the　average　SSIM　are　improved　by　11%,　76%,　and　70%,　respectively.　©　2023　SPIE.