Introduction: In this research, low-level helium-neon (He-Ne) laser irradiation effects on monkey kidney cells (Vero cell line) MITOSIS were studied.Methods: The experiment was carried out on a monkey kidney cell line "Vero (CCL-81) ". This is a lineage of cells used in cell cultures and can be used for efficacy and media testing. The monolayer cells were formed on coating glass in a spectral cuvette (20×20×30 mm). The samples divided into two groups. The first groups as irradiated monolayer cells were exposed by a He-Ne laser (PolyaronNPO, L’vov, Ukraine) with l=632.8 nm, max power density (P)=10 mW/cm2, generating linearly polarized and the second groups as the control monolayer cells were located in a cuvette protected by a lightproof screen from the first cuvette and also from the laser exposure. Then, changing functional ACTIVITY of the monolayer cells, due to the radiation influence on some physical factors were measured.Results: The results showed that low-intensity laser irradiation in the range of visible red could make meaningful changes in the cell division process (the MITOSIS ACTIVITY). These changes depend on the power density, exposure time, the presence of a magnetic field, and the duration of time after exposure termination. The stimulatory effects on the cell division within the power density of 1-6 mW/ (cm2) and exposure time in the range of 1-10 minutes was studied. It is demonstrated that the increase in these parameters (power density and exposure time) leads to destructing the cell division process.Conclusion: The results are useful to identify the molecular mechanisms caused by low-intensity laser effects on the biological activities of the cells. Thus, this study helps to optimize medical laser technology as well as achieving information on the therapeutic effects of low-intensity lasers.

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.

Counting mitotic figures present in tissue samples from a patient with cancer, plays a crucial role in assessing the patient’s survival chances. In clinical practice, mitotic cells are counted manually by pathologists in order to grade the proliferative ACTIVITY of breast tumors. However, detecting mitoses under a microscope is a labourious, time-consuming task which can benefit from computer aided diagnosis. In this research we aim to detect mitotic cells present in breast cancer tissue, using only texture and pattern features. To classify cells into mitotic and non-mitotic classes, we use an AdaBoost classifier, an ensemble learning method which uses other (weak) classifiers to construct a strong classifier.11 different classifiers were used separately as base learners, and their classification performance was recorded. The proposed ensemble classifier is tested on the standard MITOS-ATYPIA-14 dataset, where a 64×64 pixel window around each cells center was extracted to be used as training data. It was observed that an AdaBoost that used Logistic Regression as its base learner achieved a F1 Score of 0.85 using only texture features as input which shows a significant performance improvement over status quo. It is also observed that "Decision Trees" provides the best recall among base classifiers and "Random Forest" has the best Precision.