Recently, deep learning is used with convolutional Neural Networks for image classification and figure recognition. In our research, we used Computed Tomography (CT) scans to train a double convolutional Deep Neural Network (CDNN) and a regular CDNN. These topologies were tested against lung cancer images to determine the Tx cancer stage in which these topologies can detect the possibility of lung cancer. The first step was to pre-classify the CT images from the initial dataset so that the training of the CDNN could be focused. Next, we built the double Convolution deep Neural Network with max pooling to perform a more thorough search. Finally, we used CT scans of different Tx cancer stages of lung cancer to determine the Tx stage in which the CDNN would detect possibility of lung cancer. We tested the regular CDNN against our double CDNN. Using this algorithm, doctors will have additional help in early lung cancer detection and early treatment. After extensive training with 100 epochs, we obtained the highest accuracy of 0.9962, whereas the regular CDNN obtained only 0.876 accuracy.

As information society changes, the digital world is making more use of larger bulks of data and complex operations that need to be executed. This trend has caused overcoming the processor speed limit issues, by introducing multiple processor systems. In spite of hardware-level parallelism, the software has evolved with various techniques for achieving parallel programs execution. Executing a program in parallel can be efficiently done only if the program code follows certain rules. There are many techniques, which tend to provide variant processing speeds. The aim of this paper is to test the Matlab, OpenMPI and Pthreads methods on a single-processor, multi-processor, GRID and cluster systems and suggest optimal method for that particular system.