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Classification of H&E Stained Liver Histopathology Images Using Ensemble Learning Techniques for Detection of the Level of Malignancy of Hepatocellular Carcinoma (HCC)

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Advances in Artificial Intelligence-Empowered Decision Support Systems

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 39))

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Abstract

Hepatocellular carcinoma (HCC) is one of the most common types of primary liver cancer and a leading cause of cancer-related deaths worldwide. Diagnosis of the HCC using H&E stained liver histopathology images is a challenging task mainly because of the time-consuming and error-prone manual examination even when performed by skilled pathologists. Deep learning has revolutionized medical diagnosis by providing accurate and economical computer-aided diagnosis solutions. The classification of histopathology images with algorithms such as convolutional neural networks has shown promising results in recent studies. To overcome the limitations of previous studies and provide a more efficient solution, this study proposes a novel deep learning-based ensemble model. The publicly available TCGA-LIHC dataset, consisting of three cancer types based on severity level, is pre-processed and then further used for model development and evaluation. Four pre-trained CNN models AlexNet, VGG16, Inception-v3, and ResNet50 that follow transfer learning approaches are used to build the ensemble model. The proposed ensemble model outperforms the state-of-the-art CNN architectures in terms of sensitivity, specificity, F1-score, accuracy, and area under the curve (AUC). The proposed model when compared with previously published models for the same TCGA-LIHC dataset, shows a better HCC prediction accuracy than most. In conclusion, we affirm that the proposed ensemble deep learning model is able to classify H&E stained histopathology images better than the state-of-the-art CNN architectures, ultimately providing a time-saving and precise solution to the HCC classification task.

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Acknowledgements

This work was supported by the Science and Engineering Research Board, India under Grant SERB- CRG/2021/005752.

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Authors and Affiliations

  1. Visvesvarya National Institute of Technology, Nagpur, Maharashtra, India

    Adesh Rukmangad, A**kya Deshpande, Ankush Jamthikar, Deep Gupta & Ankit Bhurane

  2. Rutgers Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA

    Ankush Jamthikar

  3. All India Institute of Medical Sciences, Nagpur, Maharashtra, India

    Nisha B. Meshram

Authors
  1. Adesh Rukmangad
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  2. A**kya Deshpande
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  3. Ankush Jamthikar
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  4. Deep Gupta
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  6. Nisha B. Meshram
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Corresponding author

Correspondence to Deep Gupta .

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Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  3. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Haris Doukas

  4. KES International, SELBY, UK

    Lakhmi C. Jain

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Rukmangad, A., Deshpande, A., Jamthikar, A., Gupta, D., Bhurane, A., Meshram, N.B. (2024). Classification of H&E Stained Liver Histopathology Images Using Ensemble Learning Techniques for Detection of the Level of Malignancy of Hepatocellular Carcinoma (HCC). In: Tsihrintzis, G.A., Virvou, M., Doukas, H., Jain, L.C. (eds) Advances in Artificial Intelligence-Empowered Decision Support Systems. Learning and Analytics in Intelligent Systems, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-031-62316-5_3

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