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The computed Deng entropy, based on the box covering method, is used as a vector to feed bidirectional LSTM (bLSTM) networks and obtain Deng\u2019s information dimensions. This innovative approach obtains Deng entropy from different scales (box sizes), yielding a measure that captures differences in complexity. Three histopathological datasets (BreakHis, Lung-colon, and PANDA) were analyzed. Statistical tests were performed on each dataset to determine the most effective discrimination of histopathological images between the information and Deng information dimensions. The binary breast classification exhibited a higher performance accuracy rate of 0.98. For multiclass analysis, the accuracy was 0.99. The lung image model exceeded a classification accuracy of 0.98, and for the colon, it was 0.99. The prostate image accuracy was 0.924. Deng entropy provides a precise classification system for histopathological images of breast, colon, and lung cancers. 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