Efficient cell classification of mitochondrial images by using deep learning

Key challenges for affected cells, evolutionary biology and precision medicine include the effect of drug and understanding viscosity and intensity of drug-treated cells. However, this is extremely difficult because the enormous cells are affected by the drug. We developed a deep learning-based framework DNCIC that can accurately predict normal mitochondria and drug-affected cells that are rare or not observed. For optimization, we used a convolutional neural network and trained using a dataset of mitochondrial images, which were collected through the confocal microscope. The obtained algorithm was validated on the normal and affected cell images. We have trained CNN that can classify (normal and affected cells) two-photon excited fluorescence probe images. The proposed model has classified images and videos with 98% accuracy. Our results provided a foundation for drug-affected cell diagnosis.