Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images

Amit Kumar Chanchal, Aman Kumar, Shyam Lal, Jyoti Kini

Research output: Contribution to journalArticlepeer-review

Abstract

Image segmentation is consistently an important task for computer vision and the analysis of medical images. The analysis and diagnosis of histopathology images by using efficient algorithms that separate hematoxylin and eosin-stained nuclei was the purpose of our proposed method. In this paper, we propose a deep learning model that automatically segments the complex nuclei present in histology images by implementing an effective encoder–decoder architecture with a separable convolution pyramid pooling network (SCPP-Net). The SCPP unit focuses on two aspects: first, it increases the receptive field by varying four different dilation rates, keeping the kernel size fixed, and second, it reduces the trainable parameter by using depth-wise separable convolution. Our deep learning model experimented with three publicly available histopathology image datasets. The proposed SCPP-Net provides better experimental segmentation results compared to other existing deep learning models and is evaluated in terms of F1-score and aggregated Jaccard index.

Original languageEnglish
Article number107177
JournalComputers and Electrical Engineering
Volume92
DOIs
Publication statusPublished - 06-2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)
  • Electrical and Electronic Engineering

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