Lung carcinoma is the most prevalent type of cancer in the world, considered to be a relentlessly progressive disease, with dismal mortality rates to patients. Recent advances in targeted therapy hold the premise for the delivery of better, more effective treatments to lung cancer patients, that could significantly enhance their survival rates. Optimizing care delivery through targeted therapies requires the ability to effectively identify and diagnose lung cancer along with identifying the lung cancer cell type specific to each patient, \textit{small cell carcinoma}, \textit{adenocarcinoma}, or \textit{squamous cell carcinoma}. Label free optical imaging techniques such as the \textit{Coherent anti-stokes Raman Scattering microscopy} have the potential to provide physicians with minimally invasive access to lung tumor sites, and thus allow for better cancer diagnosis and sub-typing. To maximize the benefits of such novel imaging techniques in enhancing cancer treatment, the development of new data analysis methods that can rapidly and accurately analyze the new types of data provided through them is essential. Recent studies have gone a long way to achieving those goals but still face some significant bottlenecks hindering the ability to fully exploit the diagnostic potential of CARS images, namely, the streamlining of the diagnosis process was hindered by the lack of ability to automatically detect cancer cells, and the inability to reliably classify them into their respective cell types. More specifically, data analysis methods have thus far been incapable of correctly identifying and differentiating the different non-small cel lung carcinoma cell types, a stringent requirement for optimal therapy delivery. In this study we have addressed the two bottlenecks named above, through designing an image processing framework that is capable of, automatically and accuratly, detecting cancer cells in two and three dimensional CARS images. Moreover, we built upon this capability with a new approach at analyzing the segmented data, that provided significant information about the cancerous tissue and ultimately allowed for the automatic differential classification of non-small cell lung carcinoma cell types, with superb accuracies.