Clinical application of image processing and neural network in cytopathological diagnosis of lung cancer

Objective To study a new technique (lung cancer diagnossing system, LCDS) based on the computer imaging and artificial neural network for early diagnosis of lung cancer, and evaluate it's value in early cytopathological diagnosis of lung cancer. Methods The cytological smears from the specimens obtained by Percutaneous Aspiration Lung Biopsy (PALB) in 512 cases were synthetically analyzed by LCDS. Among them, 362 cases received operations. The diagnoses by LCDS were compared with postoperative histopathological diagnosis. Results In cytopathological diagnoses for the 512 specimens, LCDS can judge between cancer cells and non-cancer cells from lung lesions with its image analysis and expert system. Moreover, it can distinguish squamous carcinoma, adenocarcinoma and small cell carcinoma in cytopathological diagnosis with built-in neural network. The total coincident rate of LCDS diagnosis was 91.80% compared with the pathological diagnosis. In the 362 cases, the sensitivity of LCDS diagnosis was 94.79% (291/307), the specificity was 90.91%(50/55), and the consistent rate was 94.20%(341/362). Conclusion The diagnostic pattern of LCDS was practical and effective. It has applicable value in cytopathological diagnosis of lung cancer and may be an efficient means for early diagnosis of lung cancer.