Diagnose　brain　diseases　by　brain　CT　images　is　one　of　the　most　common　ways.　But,　it　usually　takes　several　(＞　7)　years　to　train　a　professional　doctor　because　it　is　very　challenging　to　diagnose　brain　diseases　cor-rectly.　The　study　on　automated　assistance　of　brain　CT　diagnosis　is　still　limited.　In　this　paper,　we　research　the　challenges　of　this　task　and　propose　a　method　by　simulating　human　doctor　diagnosis　habits.　Our　method　analyzes　a　full　slice　of　brain　CT　images,　instead　of　every　single　one,　to　take　into　account　contin-uous　changes　of　the　whole　brain　structure,　simulate　the　way　the　doctor　diagnoses.　To　avoid　redundancies　in　a　thin　slice　scan,　we　propose　a　redundancy　removal　and　data　augmentation　method　that　can　both　reduce　computation　complexity　and　improve　performance　without　information　loss.　Doctors　make　a　diagnosis　by　observing　several　key　images　and　key　points　in　them.　Our　method　achieved　this　by　two　steps　of　attention　mechanisms.　It　can　highlight　the　images　and　key　points　that　have　significant　impacts　on　the　prediction　and　explain　the　results.　We　evaluated　our　method　on　two　public　datasets　CQ500　and　RSNA,　which　achieved　0.9262　and　0.8650　F1　score　respectively.　Moreover,　an　experienced　doctor　(with　29　years　of　experience)　verified　the　promising　clinical　application　value　of　the　proposed　method　through　manual　experiments.　(c)　2021　Elsevier　B.V.　All　rights　reserved.