As　an　important　method　to　diagnose　gastric　cancer,　gastric　pathological　sections　images　(GPSI)　are　hard　and　time-consuming　to　be　recognized　even　by　an　experienced　doctor.　An　efficient　method　was　designed　to　detect　gastric　cancer　in　magnified　(20x)　GPSI　using　deep　learning　technology.　A　novel　DenseNet　architecture　was　applied,　modified　with　a　multistage　attention　module　(MSA-DenseNet).　To　develop　this　model　focusing　on　gastric　features,　a　two-stage-input　attention　module　was　adopted　to　select　more　semantic　information　of　cancer.　Moreover,　the　pretraining　process　was　divided　into　two　steps　to　improve　the　effect　of　the　attention　mechanism.　After　training,　our　method　achieved　a　state-of-the-art　performance　yielding　0.9947　F1　score　and　0.9976　ROC　AUC　on　a　test　dataset.　In　line　with　our　expectation　in　clinical　practice,　a　high　recall　(0.9929)　was　produced　with　high　sensitivity　to　the　positive　samples.　These　results　indicate　that　this　new　model　performs　better　than　current　artificial　detection　approaches　and　its　effectiveness　is　therefore　validated　in　cancer　pathological　diagnoses.