We　participate　in　the　FEMH　2018　Challenge　of　a　bigdata　subproject　of　the　IEEE.　The　goal　of　this　Challenge　is　pathological　voice　detection,　and　classify　the　different　diseases,　including　phono　trauma,　neoplasm　and　vocal　paralysis.　Final,　this　challenge　uses　sensitivity,　specificity　and　UAR　as　a　result.　The　database　is　recorded　with　50　normal　voice　samples　and　150　samples　of　common　voice　disorders　in　a　tertiary　teaching　hospital　(Far　Eastern　Memorial　Hospital,　FEMH).　The　paper　proposes　a　Deep　Neural　Networks　based　(DNN-based)　approach　in　this　challenge.　Data　preprocessing　used　Mel-Frequency　Cepstral　Coefficients　(MFCCs),　which　also　have　emotion　specific　information.　Gradual　spectral　variations　are　captured　using　13　MFCCs　extracted　from　speech　signal.　In　the　disease　detection　section,　we　examine　the　performance　among　different　DNN　structures　(ie,　hidden　layers　and　number　of　neurons).　Then,　In　the　disease　classification　section,　examine　the　performance　among　different　batch　sizes　and　normalize　or　no　normalize.　Finally,　the　tested　DNN　structures　have　the　best　results　at　5　hidden　layers　and　200　of　neurons.　©　2018　IEEE.