Dual-Input Type Convolutional Neural Networks Employing Color Normalized and Nuclei Segmented Data for Histopathology Image Classification

Osman Demirel, Muhammad Tahir Akhtar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Improvements in Convolutional Neural Network (CNN) have been widely successful for histopathology image classification. However, color normalization for data preprocessing and nuclei segmentation for feature extraction should also be considered for further performance boost, data redundancy elimination, and provision of distinguishing information. These techniques are known to improve generalizability. However, there is a need to find ways to use the data obtained from color normalized and segmented data for training. In this work, dual-input CNN (DiCNN), concatenated-input CNN (CiCNN), and ensemble CNN (ECNN) are trained and tested with color normalized and nuclei segmented data. The normalization technique is chosen based on correlation and structural similarity. The segmentation method is chosen based on the best-performing normalization technique for consistency and generalizability. The results show that normalized and segmented inputs results in better binary classification with CiCNN outperforming other methods. However, for multiclass classification raw data training is advantageous for all approaches.

Original languageEnglish
Title of host publicationProceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
PublisherIEEE Computer Society
Pages378-382
Number of pages5
ISBN (Electronic)9781665452458
DOIs
Publication statusPublished - 2023
Event22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam
Duration: Jul 2 2023Jul 5 2023

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2023-July

Conference

Conference22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/TerritoryViet Nam
CityHanoi
Period7/2/237/5/23

Keywords

  • Color Normalization
  • Dual Input CNN
  • Histopathology
  • Image Classification
  • Nuclei Segmentation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Dual-Input Type Convolutional Neural Networks Employing Color Normalized and Nuclei Segmented Data for Histopathology Image Classification'. Together they form a unique fingerprint.

Cite this