Counting mitotic cellsis one of the main tasks involved in assessing breast cancer proliferation grade. Unfortunately, detection of mitoses present in the tissue is a challenging task. These cells have a wide variety of shape configurations and are sometimes very similar to apoptotic cells or external objects in the tissue sample. Utilizing image processing for automatic detection of mitotic cells is likely to reduce human errorandincreasegrading speed and performance.Most available mitosis detection methods extract many features from cells then classify cells using classic classifiers, or else, directly classify cells using neural networks. The former are fast but inaccurate methods, the latter being slow but accurate. In this work, we aim to present a simultaneously fast and accurate method based on a special type of neural networks, called ELM. After a pre-processing step, candidate cells are selected using thresholding and finding local maxima. An ELM is then directly trained with each cell image, without feature extraction. Our results indicate a considerable improvement over the status-quo. Our method also benefits from a very fast training time and test time.