Although　deep　convolutional　neural　network　(CNN)　has　been　widely　used　in　the　breast　cancer　detection　based　on　thermal　imaging　technology,　this　scenario　still　did　not　receive　enough　attention　in　the　mobile　devices　with　limited　resource.　In　addition,　there　still　exists　challenge　on　how　to　assist　front　view　thermal　imaging　by　side　one　during　breast　cancer　detection.　This　study　proposes　a　multi-input　lightweight　CNN　named　Multi-light　Net　in　order　to　achieve　more　accurate　early　detection　for　breast　cancer,　which　combines　the　thermal　image　from　multiple　perspectives　with　the　lightweight　CNN　on　the　basis　of　model　performance　and　scale.　In　addition,　a　new　weighted　label　smoothing　regularization　(WLSR)　is　proposed　for　the　Multi-light　Net　with　the　purpose　of　increasing　the　network＇s　generalization　ability　and　classification　accuracy.　The　experimental　results　demonstrate　that　the　proposed　approach　by　combining　front　view　with　side　view　can　achieve　more　significant　results　than　the　common　one　using　only　front　view　during　breast　cancer　detection,　and　the　proposed　Multi-light　Net　also　exhibits　an　excellent　performance　with　respect　to　the　currently　popular　lightweight　CNN.　Furthermore,　the　proposed　WLSR　loss　function　can　also　lead　to　both　faster　convergence　rate　and　more　stable　training　process　during　network　training　and　ultimately　higher　diagnostic　accuracy　for　breast　cancer.