Lung cancer, an uncontrolled development of abnormal cells in one or both lungs has been one of the primary causes of cancer related deaths worldwide. Detecting it in the earlier stage is the only solution to reduce lung cancer deaths. The most common tests to look for cancerous cells include X-ray, CT scan, Sputum cytology and biopsy test. CT scan is recognized as one of the effective tools in recognizing it in the earlier stage. Detecting the lung nodules (lesions) sometimes seems to be very difficult in Computer Aided Detection (CAD) systems. Because of the fact that the lung nodules have similar contrast with other structure, there might be a chance in generating numerous false positives. The performance of Convolutional Neural Network (CNN) mainly depends on the hyper parameters selected for a problem. The main motive of the proposed work is to use Bat algorithm to optimize the network hyper parameters such as number of filters in convolution layers, number of neurons and filter size in the CNN to enhance the network performance thereby eliminating the requirement of manual search for optimal hyper parameters. The methodology is validated using important performance validation metrics such as accuracy, sensitivity and specificity. The result shows that CNN in conjunction with Bat algorithm provides better results in the classification of nodules and non-nodules with minimal false positive rate.