Deep learning is an effective method for medical object detection. Studies show that deep networks can achieve accurately in medical segmentation and detection tasks. This is due to the depth and training methods of deep networks which allow them to derive different levels of abstractions of input mages. In this paper, the left ventricle detection task is carried out using a deep network called stacked auto-encoder (SAE). The networks take off this task as a binary classification task wherein left and non-left ventricles cropped images are being recognized by the SAE. Once, the network recognizes left and non-left ventricles, the whole task starts by initiating a sliding window that moves through the whole magnetic resonance (MR) slice till a left ventricle is detected. Experimentally, the network showed effective detection performance when target images are noisy as it is seen that it is capable of detecting left ventricles in target images with up to 10% of salt and pepper noise.