Developing an automated lung disease diagnosis framework is still remains one of the most challenging and demanding tasks in recent days. Most of the medical experts highly preferring the Computed Tomography (CT) lung images for an accurate disease detection. For this purpose, various segmentation, optimization, and classification techniques are developed in the conventional works for lung pulmonary disease detection. However, the existing techniques have the major problems of over segmentation, inaccurate ROI extraction, reduced accuracy, computational complexity, and high false positives. Thus, this research work intends to a simple and efficient segmentation based classification framework for an accurate lung nodules detection and pulmonary disease classification. Here, the tanh normalization technique is applied for preprocessing the input lung CT image with reduced noise and increased quality. After that, the perceptual U-Net segmentation algorithm is employed to accurately segment the lung nodules from the preprocessed CT images with simple computational operations. Moreover, the Decked Dragonfly Optimization (DDO) technique is used for choosing the relevant features based on the best optimal solution, which supports to obtain an increased detection accuracy and reduced classification error rate. Finally, the Speculative Deceptive Network (SDN) based classification algorithm is deployed to exactly detect the pulmonary lung cancer according to the optimal features. During evaluation, the performance of the proposed segmentation based DDO-SDN mechanism is validated and compared by using various evaluation parameters.