The　Novel　Coronavirus　2019　(COVID-19)　is　a　global　pandemic　which　has　a　devastating　impact.　Due　to　its　quick　transmission,　a　prominent　challenge　in　confronting　this　pandemic　is　the　rapid　diagnosis.　Currently,　the　commonly-used　diagnosis　is　the　specific　molecular　tests　aided　with　the　medical　imaging　modalities　such　as　chest　X-ray　(CXR).　However,　with　the　large　demand,　the　diagnoses　of　CXR　are　time-consuming　and　laborious.　Deep　learning　is　promising　for　automatically　diagnosing　COVID-19　to　ease　the　burden　on　medical　systems.　At　present,　the　most　applied　neural　networks　are　large,　which　hardly　satisfy　the　rapid　yet　inexpensive　requirements　of　COVID-19　detection.　To　reduce　huge　computation　and　memory　demands,　in　this　paper,　we　focus　on　implementing　lightweight　networks　for　COVID-19　detection　in　CXR.　Concretely,　we　first　augment　data　based　on　clinical　visual　features　of　CXR　from　expertise.　Then,　according　to　the　fact　that　all　the　input　data　are　CXR,　we　design　a　targeted　four-layer　network　with　either　11　x　11　or　3　x　3　kernels　to　recognize　regional　features　and　detail　features.　A　pruning　criterion　based　on　the　weights　importance　is　also　proposed　to　further　prune　the　network.　Experiments　on　a　public　COVID-19　dataset　validate　the　effectiveness　and　efficiency　of　the　proposed　method.