Dataset Information

Non-annotated renal histopathological image analysis with deep ensemble learning.

ABSTRACT:

Background

Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients' survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage.

Methods

In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using the pre-trained CNNs and lastly perform the deep ensemble learning to determine the final classification. The heterogeneous ensemble models consisted of CNN models from five deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet. These CNN models were fine-tuned and used as base learners, they exhibited different performances and had great diversity in histopathological image analysis. The CNN models with superior classification accuracy (Acc) were then selected to undergo ensemble learning for the final classification. The performance of the investigated ensemble approaches was evaluated against the state-of-the-art literature.

Results

The performance evaluation demonstrated the superiority of the proposed best performing ensembled model: five-CNN based weighted averaging model, with an Acc (99%), specificity (Sp) (98%), F1-score (F1) (99%) and area under the receiver operating characteristic (ROC) curve (98%) but slightly inferior recall (Re) (99%) compared to the literature.

Conclusions

The outstanding robustness of the developed ensemble model with a superiorly high-performance scores in the evaluated metrics suggested its reliability as a diagnosis system for assisting the pathologists in analyzing the renal histopathological tissues. It is expected that the proposed ensemble deep CNN models can greatly improve the early detection of renal cancer by making the diagnosis process more efficient, and less misdetection and misdiagnosis; subsequently, leading to higher patients' survival rate.

SUBMITTER: Koo JC

PROVIDER: S-EPMC10498232 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Non-annotated renal histopathological image analysis with deep ensemble learning.

Koo Jia Chun JC Ke Qi Q Hum Yan Chai YC Goh Choon Hian CH Lai Khin Wee KW Yap Wun-She WS Tee Yee Kai YK

Quantitative imaging in medicine and surgery 20230721 9

<h4>Background</h4>Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients' survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage.<h4>Methods</h4>In this work, we developed deep convolutional neural network (CNN) based heterogen ...[more]

PMID: 37711826

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Dataset Information

Non-annotated renal histopathological image analysis with deep ensemble learning.

Background

Methods

Results

Conclusions

Publications

Non-annotated renal histopathological image analysis with deep ensemble learning.

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