eprintid: 10185511
rev_number: 10
eprint_status: archive
userid: 699
dir: disk0/10/18/55/11
datestamp: 2024-01-16 15:51:25
lastmod: 2025-01-13 11:15:25
status_changed: 2024-01-16 15:51:25
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Sharma, Shallu
creators_name: Kumar, Sumit
creators_name: Sharma, Manoj
creators_name: Kalkal, Ashish
title: An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F45
keywords: Breast cancer; Ensemble model; Pathology; CNN; Stain-normalization
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Histopathological diagnosis is the mainstay of present-day preventive medical care service to guide the therapy and treatment of breast cancer at an early stage. Manual examination of histologic data based on clinicians’ subjective knowledge is a time-consuming, labour-intensive, and costly method that necessitates clinical intervention and competence for a fair decision. In the recent work, we have developed an ensemble of five deep CNNs to classify three grades of breast cancer using quantitative image-based assessment of digital pathology slides without any manual intervention. To produce final predictions on the dataset, a fuzzy ranking algorithm is used. On the Databiox dataset, the suggested model attained an accuracy of 79%, 75%, 89%, and 82% at 4×, 10×, 20×, and 40× magnification, respectively. Furthermore, it has been observed that the stain-normalization strategy improves the model’s classification performance on the histopathological images. In this case, the Macenko stain-normalization technique is employed which further enhances the performance of the proposed ensemble model up to 80%, 100%, 100%, and 82% at 4×, 10×, 20×, and 40× magnification, respectively. Additionally, a comparative analysis with the existing state-of-the-art technique demonstrated the superiority of the proposed scheme.
date: 2024-04
date_type: published
publisher: Springer Science and Business Media LLC
official_url: http://dx.doi.org/10.1007/s00521-023-09368-1
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2139768
doi: 10.1007/s00521-023-09368-1
lyricists_name: Kalkal, Ashish
lyricists_id: AKALK88
actors_name: Kalkal, Ashish
actors_id: AKALK88
actors_role: owner
full_text_status: public
publication: Neural Computing and Applications
volume: 36
pagerange: 5673-5693
issn: 0941-0643
citation:        Sharma, Shallu;    Kumar, Sumit;    Sharma, Manoj;    Kalkal, Ashish;      (2024)    An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images.                   Neural Computing and Applications , 36    pp. 5673-5693.    10.1007/s00521-023-09368-1 <https://doi.org/10.1007/s00521-023-09368-1>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10185511/1/accepted-manuscript_NCAA.pdf