Identification of Malignancy from Cytological Images Based on Superpixel and Convolutional Neural Networks

  • Shyamali Mitra
  • Soumyajyoti Dey
  • Nibaran DasEmail author
  • Sukanta Chakrabarty
  • Mita Nasipuri
  • Mrinal Kanti Naskar
Part of the Studies in Computational Intelligence book series (SCI, volume 784)


This chapter explores two methodologies for classification of cytology images into benign and malignant. Heading toward the automated analysis of the images to eradicate human intervention, this chapter draws curtain from the history of automated CAD-based design system for better understanding of the roots of the evolving image processing techniques in the analysis of biomedical images. Our first approach introduces the clustering-based approach to segment the nucleus region from the rest. After segmentation, nuclei features are extracted based on which classification is done using some standard classifiers. The second perspective suggests the usage of deep-learning-based techniques such as ResNet and InceptionNet-v3. In this case, classification is done with and without segmented images but not using any handcrafted features. The analysis provides results in favor of CNN where the average performances are found better than the existing result using feature-based approach.


Cytology FNAC Superpixel-based segmentation ResNet50 InceptionNet-V3 Random crop Random horizontal flip 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shyamali Mitra
    • 1
  • Soumyajyoti Dey
    • 2
  • Nibaran Das
    • 2
    Email author
  • Sukanta Chakrabarty
    • 3
  • Mita Nasipuri
    • 2
  • Mrinal Kanti Naskar
    • 1
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  3. 3.Theism Medical Diagnostics CentreKolkataIndia

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