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Gradient-Guided Robust Adaptive Multiclass Object Detection in Sputum Cell Images

by Yushu Liu, Wenhao Yi, Yuhua Feng, Ming Zhang, Di Gai, Weiping Zhang, Xianhao Zhong
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 148
Year of Publication: 2026
Authors: Yushu Liu, Wenhao Yi, Yuhua Feng, Ming Zhang, Di Gai, Weiping Zhang, Xianhao Zhong
10.5120/ijca7c2db64981de

Yushu Liu, Wenhao Yi, Yuhua Feng, Ming Zhang, Di Gai, Weiping Zhang, Xianhao Zhong . Gradient-Guided Robust Adaptive Multiclass Object Detection in Sputum Cell Images. International Journal of Computer Applications. 187, 148 ( Oct 2026), 46-53. DOI=10.5120/ijca7c2db64981de

@article{ 10.5120/ijca7c2db64981de,
author = { Yushu Liu, Wenhao Yi, Yuhua Feng, Ming Zhang, Di Gai, Weiping Zhang, Xianhao Zhong },
title = { Gradient-Guided Robust Adaptive Multiclass Object Detection in Sputum Cell Images },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2026 },
volume = { 187 },
number = { 148 },
month = { Oct },
year = { 2026 },
issn = { 0975-8887 },
pages = { 46-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number148/gradient-guided-robust-adaptive-multiclass-object-detection-in-sputum-cell-images/ },
doi = { 10.5120/ijca7c2db64981de },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-10-24T03:08:47+05:30
%A Yushu Liu
%A Wenhao Yi
%A Yuhua Feng
%A Ming Zhang
%A Di Gai
%A Weiping Zhang
%A Xianhao Zhong
%T Gradient-Guided Robust Adaptive Multiclass Object Detection in Sputum Cell Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 148
%P 46-53
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automated sputum cell detection supports quantitative assessment of respiratory inflammation, but clinical images contain overlapping targets, heterogeneous backgrounds, large scale variation, and imbalanced classes. This study presents GRA-InternRTM, a multiclass detector based on InternImage-RTMDet. An Edge-Guided Deformable Adapter (EGDA) strengthens boundary representations, and an Adaptive Gated Fusion Module (AGFM) provides sample-specific multiscale fusion. Dynamic Head (DyHead) aligns features across scales, while Dual-Robust Learning loss (DRL loss) reduces the effects of class imbalance and missing annotations. Ablation experiments evaluated the four components on an in-house dataset. Compared with six reference detectors, GRA-InternRTM achieved mAP, mAP50, Recall, and AccMatched values of 0.365, 0.622, 0.8762, and 0.9617, respectively. Relative to Fast R-CNN, it improved mAP, mAP50, and AccMatched by 0.014, 0.024, and 0.0308. These results support the technical feasibility of multiclass detection in complex sputum cell images, although external validation remains necessary.

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Index Terms

Computer Science
Information Sciences

Keywords

Sputum cell images object detection InternImage EGDA AGFM DyHead

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