The core of deep learning which is convolution neural network (CNN) has been widely adopted in image processing and object recognition areas. In particular, medical imaging requires very precise, accurate and fine recognition power. Numerous works in the literature have reported promising prospects of CNN applied in prognosis and radiology diagnosis. A common goal among those works, largely is to try achieving a most accurate deep learning model in analysing the insights from the finest details of the medical imaging. To this end, a novel machine learning model that is equipped with multi-view data-preprocessing and swarm-based hyperparameter optimization is proposed. The former is for provi ding additional training data in the hope that salient features could discovered; the latter is for finding the most optimal set of model parameters for the CNN. They both serve only one purpose-to enhance the object recognition power to the highest possible. Preliminary experiments over datasets related to heart disease detection and breast cancer classification over CTG and mammograms respectively indicate encouraging results.