In recent time the Lung Cancer disease is evolving as highly life-threatening disease for human beings, as per world-health organization the lung-cancer disease becoming second largest cause of deaths as compared to all other types of cancer. Although the prevailing available technology is striving to get more exposer in the area of medical science using Computer-Assisted Diagnosis (CAD) system, where image processing is playing crucial role for detecting the cancerous nodules in computer tomographic images. Augmenting the Machine Learning techniques with image processing algorithms is becoming more comprehensive examination of cancer disease in proposed CAD system. This paper is exhibiting the heuristic approach for lung cancer nodule detection, the purported model is predominantly categorized the foremost tasks, which are Image Enhancement, Segmenting ROI (Region of Interest), Features Extraction, Nodule Classification. In preprocessing, primarily the AMF filtering method is applied to eliminate the speckle noise in input CT image of LIDC-IDRI dataset through, and quality of input image is improved by applying Histogram Equalization technique with Contrast-Limited Adaptive approach. Secondly, in successive stage the Improved LevelSet (ILS) algorithm is used to segment the interest region (ROI). Furthermore, the third step of projected work is applied to extract the definite learnable Texture Features and Statistical Features from segmented ROI. Based on the extracted features in aforementioned stage are applied to pioneering improved Convolutional Neural Network CNN architecture with Hybrid-Layer to classify the lung cancer nodule is either benign or malignant. Principally this researchis carried out by contributing to each stage of it, where novel concept of improved Hybrid-Layer Convolutional Neural Network (CNN) is employed by optimizing and selecting the optimal weight using the Enhanced Cat Swarm Optimization (ECSO) algorithm. The experimental result of proposed Hybrid-Layer CNN using weight optimization algorithm ECSO is achieved the accuracy of 93.5%, which is comparatively efficient with respect to existing model such as DBN, SVM, CNN, Hybrid-Layer: WOA, MFO, CSO. Moreover, this work provides conclusive decision on detected nodule is either benign or malignant.