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A huge scope of lung surface disease examples can be seen in CT scan examinations. These pictures are the intermixed combination of different examples and henceforth it turns out to be extremely hard for Radiologists to separate among them and analyze the disease. One method for understanding this issue is the utilization of Diabetic Neural Network (DNN). DNN is utilized for design grouping and image recognition frameworks. They have accomplished less error on the database and the image classification utilizing DNN was shockingly quick. The CT Scan Images utilized in this investigation are authoritatively checked by the ensured Radiologist. The previous proposal discusses the identification of different levels of diabetics in a patient and continuation is described in this research. This proposed research work is aimed to design a classifier system for lung disease diagnosis of diabetic patients using Diabetic Neural Networks (DNN) when the Fundus Image of the Diabetic patient is given as input the affected range and desired disease of lungs for various level diabetic stages are detected. The classifier system described in this research is developed for the mass screening of patients related to the diabetic. This classifier system is developed to detect various lung diseases such as Asthma, pneumothorax or atelectasis, bronchitis, COPD, Lung cancer, pneumonia, etc.