Recently,　deep　neural　networks　have　demonstrated　their　efficiency　in　image　classification　tasks,　which　are　commonly　achieved　by　an　extended　depth　and　width　of　network　architecture.　However,　poor　convergence,　over-fitting　and　gradient　disappearance　might　be　generated　with　such　comprehensive　architectures.　Therefore,　DenseNet　is　developed　to　address　these　problems.　Although　DenseNet　adopts　bottleneck　technique　in　DenseBlocks　to　avoid　relearning　feature-maps　and　decrease　parameters,　this　operation　may　lead　to　the　skip　and　loss　of　important　features.　Besides,　it　still　takes　oversized　computational　power　when　the　depth　and　width　of　the　network　architecture　are　increased　for　better　classification.　In　this　paper,　we　propose　a　variate　of　DenseNet,　named　Multipath　Feature　Recalibration　DenseNet　(MFR-DenseNet),　to　stack　convolution　layers　instead　of　adopting　bottleneck　for　improving　feature　extraction.　Meanwhile,　we　build　multipath　DenseBlocks　with　Squeeze-Excitation　(SE)　module　to　represent　the　interdependencies　of　useful　feature-maps　among　different　DenseBlocks.　Experiments　in　CIFAR-10,　CIFAR-100,　MNIST　and　SVHN　reveal　the　efficiency　of　our　network,　with　further　reduced　redundancy　whilst　maintaining　the　high　accuracy　of　DenseNet.