One　of　the　main　areas　of　study　in　diagnostic　radiology　and　medical　imaging　is　computer-aided　diagnosis　(CAD).　In　reality,　a　significant　number　of　CAD　systems　have　been　used　to　help　doctors　identify　breast　tumours　early　on　mammograms.　Medical　image　analysis　algorithms　are　helpful　in　providing　a　better　and　more　accurate　comprehension　of　medical　images　as　well　as　in　boosting　the　dependability　of　medical　images　in　the　healthcare　system　because　therapy　and　illness　diagnosis　are　so　crucial　in　medical　imaging.　Leveraging　advanced　machine　learning　techniques　has　become　an　invaluable　tool　for　healthcare　professionals,　enhancing　early　detection　and　personalizing　treatment　plans.Therefore,　in　this　work,　we　began　by　mentioning　several　cutting-edge　techniques　for　detecting　breast　cancer　using　a　deep　learning　methodology.　The　primary　drawback　of　current　research　is　that　existing　models　only　concentrate　on　identifying　or　detecting　benign　or　malignant　tumours,　rather　than　specific　types　of　tumours　such　adenosis,　phyllodes　tumour,　or　lobular　carcinoma.　We　used　a　number　of　lightweight　deep　learning　models,　like　ShuffleNet,　to　create　a　flexible　model.　[11]Additionally,　in　order　to　achieve　recognition,　we　create　the　Resnet50　classical　CNN　model　based　on　transfer　learning.By　incorporating　transfer　learning,　the　model　can　effectively　use　pre-trained　networks　to　enhance　its　learning　capability,　potentially　yielding　better　performance　than　training　from　scratch.number　of　lightweight　deep　learning　models,　like　ShuffleNet,　to　create　a　flexible　model.　[4]　A　new　multi-label　breast　cancer　tissues　classification　model　that　combines　the　benefits　of　Resnet　and　the　Attention　mechanism　is　also　taken　into　consideration.This　innovative　approach　is　designed　to　focus　on　specific　regions　of　interest　within　the　images,　which　can　potentially　lead　to　a　higher　accuracy　in　detecting　subtler　signs　of　various　tumour　types.　©　2023　ACM.