Efficient cell classification of mitochondrial images by using deep learning

  • Muhammad Shahid IqbalEmail author
  • Saeed El-Ashram
  • Sajid Hussain
  • Tamoor Khan
  • Shujian Huang
  • Rashid Mehmood
  • Bin Luo
Research Article


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.


Cell classification Mitochondria Deep learning and drug 



The authors are grateful to the School of Computer Sciences, Anhui University Hefei, China, for their support and cooperation.

Author contributions

MSI, SEA, SH, TK, RM and BL developed the model and analyzed the data. SH analyzed the data, made new compounds and provided the dataset of normal and drug images. All authors read and approved the final version of the manuscript.


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Copyright information

© The Optical Society of India 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.College of Life Science and EngineeringFoshan UniversityFoshanChina
  3. 3.Faculty of ScienceKafrelsheikh UniversityKafr El SheikhEgypt
  4. 4.School of Applied Sciences and Humanities (NUSASH)National University of TechnologyIslamabadPakistan
  5. 5.School of Economic Information EngineeringSouthwestern University of Finance and EconomicsChengduPeople’s Republic of China
  6. 6.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina

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