Data　is　the　essential　fuel　for　deep　neural　networks　(DNNs),　and　its　quality　affects　the　practical　performance　of　DNNs.　In　real-world　training　scenarios,　the　successful　generalization　performance　of　DNNs　is　severely　challenged　by　noisy　samples　with　incorrect　labels.　To　combat　noisy　samples　in　image　classification,　numerous　methods　based　on　sample　selection　and　semi-supervised　learning　(SSL)　have　been　developed,　where　sample　selection　is　used　to　provide　the　supervision　signal　for　SSL,　achieving　great　success　in　resisting　noisy　samples.　Due　to　the　necessary　warm-up　training　on　noisy　datasets　and　the　basic　sample　selection　mechanism,　DNNs　are　still　confronted　with　the　challenge　of　memorizing　noisy　samples.　However,　existing　methods　do　not　address　the　memorization　of　noisy　samples　by　DNNs　explicitly,　which　hinders　the　generalization　performance　of　DNNs.　To　alleviate　this　issue,　we　present　a　new　approach　to　combat　noisy　samples.　First,　we　propose　a　memorized　noise　detection　method　to　detect　noisy　samples　that　DNNs　have　already　memorized　during　the　training　process.　Next,　we　design　a　noise-excluded　sample　selection　method　and　a　noise-alleviated　MixMatch　to　alleviate　the　memorization　of　DNNs　to　noisy　samples.　Finally,　we　integrate　our　approach　with　the　established　method　DivideMix,　proposing　Modified-DivideMix.　The　experimental　results　on　CIFAR-10,　CIFAR-100,　and　Clothing1M　demonstrate　the　effectiveness　of　our　approach.