Biomedical　image　segmentation　and　classification　are　critical　components　in　a　computer-aided　diagnosis　system.　However,　various　deep　convolutional　neural　networks　are　trained　by　a　single　task,　ignoring　the　potential　contribution　of　mutually　performing　multiple　tasks.　In　this　paper,　we　propose　a　cascaded　unsupervised-based　strategy　to　boost　the　supervised　CNN　framework　for　automated　white　blood　cell　(WBC)　and　skin　lesion　segmentation　and　classification,　called　CUSS-Net.　Our　proposed　CUSS-Net　consists　of　an　unsupervised-based　strategy　(US)　module,　an　enhanced　segmentation　network　named　E-SegNet,　and　a　mask-guided　classification　network　called　MG-ClsNet.　On　the　one　hand,　the　proposed　US　module　produces　coarse　masks　that　provide　a　prior　localization　map　for　the　proposed　E-SegNet　to　enhance　it　in　locating　and　segmenting　a　target　object　accurately.　On　the　other　hand,　the　enhanced　coarse　masks　predicted　by　the　proposed　E-SegNet　are　then　fed　into　the　proposed　MG-ClsNet　for　accurate　classification.　Moreover,　a　novel　cascaded　dense　inception　module　is　presented　to　capture　more　high-level　information.　Meanwhile,　we　adopt　a　hybrid　loss　by　combining　a　dice　loss　and　a　cross-entropy　loss　to　alleviate　the　imbalance　training　problem.　We　evaluate　our　proposed　CUSS-Net　on　three　public　medical　image　datasets.　Experiments　show　that　our　proposed　CUSS-Net　outperforms　representative　state-of-the-art　approaches.