In this thesis, the applications of deep learning in the analysis, detection and classification of medical imaging datasets were studied, with a focus on datasets having a limited sample size. A combined machine learning-deep learning model was designed to classify one small dataset, prostate cancer provided by Mayo Clinic, Arizona. Deep learning model was implemented to extract imaging features followed by machine learning classifier for prostate cancer diagnosis. The results were compared against models trained on texture-based features, namely gray level co-occurrence matrix (GLCM) and Gabor. Some of the challenges of performing diagnosis on medical imaging datasets with limited sample sizes, have been identified. Lastly, a set of future works have been proposed. Keywords: Deep learning, radiology, transfer learning, convolutional neural network.