To solve the missed diagnosis of small pulmonary nodules in medical images

a new approach on computer-aided diagnosis for lung cancer based on chest CT images has been proposed.The method firstly segments the Region of Interest(ROI)

and extracts ROI’s features.Then it selects effective attributes by theory of Rough Set(RS).Finally

it constructs a specific-demand oriented recognition model for lung cancer based on these effective features.Especially

we take the Self-Organizing Neural Network(SONN)to construct the recognition model of lung cancer for fast diagnosis.In order to perform the accurate diagnosis

we need to use the Self-Adaptive Probabilistic Model(SAPM)to build lung cancer and non-cancer recognition models respectively and we can identify the classification by the similarity of the recognition sample with the model.When the similarity is small

we re-identify the lung cancer by Hidden Markov Model(HMM).The experiment results proved that the approach mentioned in this paper can hold high efficiency.