Accurate　and　fast　histological　diagnosis　of　cancers　is　crucial　for　successful　treatment.　The　deep　learning-based　approaches　have　assisted　pathologists　in　efficient　cancer　diagnosis.　The　remodeled　microenvironment　and　field　cancerization　may　enable　the　cancer-specific　features　in　the　image　of　non-cancer　regions　surrounding　cancer,　which　may　provide　additional　information　not　available　in　the　cancer　region　to　improve　cancer　diagnosis.　Here,　we　proposed　a　deep　learning　framework　with　fine-tuning　target　proportion　towards　cancer　surrounding　tissues　in　histological　images　for　gastric　cancer　diagnosis.　Through　employing　six　deep　learning-based　models　targeting　region-of-interest　(ROI)　with　different　proportions　of　no-cancer　and　cancer　regions,　we　uncovered　the　diagnostic　value　of　non-cancer　ROI,　and　the　model　performance　for　cancer　diagnosis　depended　on　the　proportion.　Then,　we　constructed　a　model　based　on　MobileNetV2　with　the　optimized　weights　targeting　non-cancer　and　cancer　ROI　to　diagnose　gastric　cancer　(DeepNCCNet).　In　the　external　validation,　the　optimized　DeepNCCNet　demonstrated　excellent　generalization　abilities　with　an　accuracy　of　93.96%.　In　conclusion,　we　discovered　a　non-cancer　ROI　weight-dependent　model　performance,　indicating　the　diagnostic　value　of　non-cancer　regions　with　potential　remodeled　microenvironment　and　field　cancerization,　which　provides　a　promising　image　resource　for　cancer　diagnosis.　The　DeepNCCNet　could　be　readily　applied　to　clinical　diagnosis　for　gastric　cancer,　which　is　useful　for　some　clinical　settings　such　as　the　absence　or　minimum　amount　of　tumor　tissues　in　the　insufficient　biopsy.