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Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs – Scientific Reports
- Article
- Open access
- Published:
- Nitin Kumar Chauhan1,
- Amit Kumar2,
- Ankit Jain1,
- Krishna Singh3,
- Dharmendra Kumar4,
- Shashank Sheshar Singh5 &
- …
- Harish Kumar Shakya6
Scientific Reports , Article number: (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Subjects
Abstract
Nowadays, computer-aided diagnostic (CAD) systems powered by artificial intelligence (AI) are becoming increasingly prevalent in cervical cancer diagnosis. Automatic selection of features by the deep convolutional neural networks (CNN) is a more prominent substitute than the conventional machine learning (ML) models, as they require handcrafted cell segmentation and extraction. Pre-trained deep models using transfer learning and fine-tuning make these models faster and more efficient, even with the limited data availability. This article proposes a novel hybrid deep network with progressive resizing (HDNPR) for classifying whole slide images (WSI) of pap smear slides. This model trains fine-tuned deep learning (DL) models on pre-trained weights utilizing progressively resized and augmented training data of size 224 × 224, 512 × 512, and 1024 × 1024 pixels, laid over with transfer learning. The hybrid deep features produced by the concatenation of extracted features from two prevalent fine-tuned deep learning networks, VGG-16 and ResNet-152, are applied to the fully connected network (FCN) stage for detecting different classes of cervical cancer. The proposed HDNPR network is evaluated for both multiclass and binary classification, and it is able to attain an accuracy score of 97.45% for 5-class classification and 98.58% for 2-class classification, respectively.
Abbreviations
- CAD:
-
Computer-aided diagnostic
- AI:
-
Artificial Intelligence
- CNN:
-
Convolutional neural networks
- ML:
-
Machine learning
- HDNPR:
-
Hybrid deep networkwith progressive resizing
- WSI:
-
Whole slide images
- DL:
-
Deep learning
- HPV:
-
Humanpapillomavirus
- Mask-RCNN:
-
Mask-recurrent neural network
- LFCCRF:
-
Local fullyconnected conditional random field
- ATT-UNet:
-
Attention U-net
- IR-Net:
-
Instance relationnetwork
- FCNNs:
-
Fully convolutional neural networks
- ELM:
-
Extreme learning machine
- TCT:
-
ThinPrep cytologic test
- DTCWT:
-
Dual-tree complex wavelet transform
- SVM:
-
Support vector machine
- FCN:
-
Fully connected network
- CCD:
-
Charge-coupled device
- SI:
-
Superficial-intermediate
- P:
-
Parabasal
- K:
-
Koilocytes
- D:
-
Dyskeratotic
- M:
-
Metaplastic
- ANN:
-
Artificial neural network
- GPU:
-
Graphics processing unit
Acknowledgements
The authors would like to acknowledge that language editing tools (Grammarly and QuillBot) were used to only assist with grammar and spelling refinement during the preparation of this manuscript.
Funding
Open access funding provided by Manipal University Jaipur. The authors received no specific funding for this study.
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The authors declare no competing interests.
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This paper does not contain any studies with human participants or animals performed by any of the authors. It does not require ethical approval.
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Chauhan, N.K., Kumar, A., Jain, A. et al. Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs. Sci Rep (2026). https://doi.org/10.1038/s41598-026-49654-1
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DOI: https://doi.org/10.1038/s41598-026-49654-1
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