Diffuse　optical　tomography　(DOT)　is　a　promising　non-invasive　optical　imaging　technique　that　can　provide　functional　information　of　biological　tissues.　Since　diffuse　light　undergoes　multiple　scattering　in　biological　tissues　and　boundary　measurements　are　limited,　DOT　reconstruction　is　ill-posedness　and　ill-conditioned.　To　overcome　these　limitations,　Tikhonov　regularization　is　the　most　popular　algorithm.　Recently,　deep　learning　based　reconstruction　methods　have　attracted　increasing　attention,　and　promising　results　have　been　reported.　However,　they　lack　generalization　for　unstructured　physical　model.　Therefore,　a　model-base　convolution　neural　network　framework　(Model-CNN)　is　developed.　It　composes　of　two　layers:　data　consistency　layer　and　depth　layer,　which　increases　the　interpretability　of　the　model.　Its　performance　is　evaluated　with　numerical　simulations.　Our　results　demonstrate　that　Model-CNN　can　get　better　reconstructed　results　than　those　obtained　by　Tikhonov　Regularization　in　terms　of　ABE,　MSE,　and　PSNR.