In　this　paper,　we　propose　a　diagnosis　and　classification　method　of　hydrocephalus　computed　tomography　(CT)　images　using　deep　learning　and　image　reconstruction　methods.　The　proposed　method　constructs　pathological　features　differing　from　the　other　healthy　tissues.　This　method　tries　to　improve　the　accuracy　of　pathological　images　identification　and　diagnosis.　Identification　of　pathological　features　from　CT　images　is　an　essential　subject　for　the　diagnosis　and　treatment　of　diseases.　However,　it　is　difficult　to　accurately　distinguish　pathological　features　owing　to　the　variability　of　appearances,　fuzzy　boundaries,　heterogeneous　densities,　shapes　and　sizes　of　lesions,　etc.　Some　study　results　reported　that　the　ResNet　network　has　a　better　classification　and　diagnosis　performance　than　other　methods,　and　it　has　broad　application　prospectives　in　the　identification　of　CT　images.　We　use　an　improved　ResNet　network　as　a　classification　model　with　our　proposed　image　reconstruction　and　information　fusion　methods.　First,　we　evaluate　a　classification　experiment　using　the　hydrocephalus　CT　image　datasets.　Through　the　comparative　experiments,　we　found　that　gradient　features　play　an　important　role　in　the　classification　of　hydrocephalus　CT　images.　The　classification　effect　of　CT　images　with　small　information　entropy　is　excellent　in　the　evaluation　of　hydrocephalus　CT　images.　A　reconstructed　image　containing　two　channels　of　gradient　features　and　one　channel　of　LBP　features　is　very　effective　in　classification.　Second,　we　apply　our　proposed　method　in　classification　experiments　on　CT　images　of　colonography　polyps　for　an　evaluation.　The　experimental　results　have　consistency　with　the　hydrocephalus　classification　evaluation.　It　shows　that　the　method　is　universal　and　suitable　for　classification　of　CT　images　in　these　two　applications　for　the　diagnosis　of　diseases.　The　original　features　of　CT　images　are　not　ideal　characteristics　in　classification,　and　the　reconstructed　image　and　information　fusion　methods　have　a　great　effect　on　CT　images　classification　for　pathological　diagnosis.