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Deep learning-based approaches for robust classification of cervical cancer

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Abstract

Cervical cancer is the fourth most common cancer worldwide, and early diagnosis is crucial for successful treatment, as with all types of cancer. The pap-smear test is considered the gold standard for diagnosing cervical cancer. However, the success of diagnosis depends on the expertise and effort of the physician, as with all cancer types. Computer-aided diagnosis systems aim to improve the speed and accuracy of cancer diagnosis by constantly improving medical image analysis and diagnosis. One of the main challenges in classifying cervical cancer with deep learning-based methods is the availability and quality of data, as well as the variability in size, shape, and appearance of cervical cancer images. This study presents effective techniques for overcoming these challenges and developing a more efficient diagnostic system. Specifically, the study applies the latest and most powerful deep learning techniques in two categories: convolutional neural network (CNN) approaches and vision transformer (ViT) approaches. The study also utilizes data augmentation techniques to increase data diversity and ensemble learning techniques to improve the accuracy of model outputs. This study presents a detailed comparison and the most extensive study in the literature by applying 40 CNN-based models and more than 20 ViT-based models on SIPaKMeD pap-smear dataset. The experimental results show that the latest ViT-based models perform better, and the existing CNN models perform similarly to the ViT models. By utilizing data augmentation and ensemble learning techniques in ViT-based models, the research exceeds previous studies and attains a level of success that has potential to be implemented in clinical settings. This progress is expected to bring down the mortality rate by enabling the early and precise identification of cancer.

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Data availability

The SIPaKMeD data that support the findings of this study are available in [repository name: “https://www.kaggle.com/datasets/prahladmehandiratta/cervical-cancer-largest-dataset-sipakmed”] with the identifier(s) [data DOI(s): “http://doi.org/10.1109/ICIP.2018.8451588”] [10].

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  1. Computer Engineering Department, Engineering Faculty, Igdir University, Igdir, Turkey

    Ishak Pacal

  2. Software Engineering Department, Engineering Faculty, Bandirma Onyedi Eylul University, Balikesir, Turkey

    Serhat Kılıcarslan

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  1. Ishak Pacal
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Pacal, I., Kılıcarslan, S. Deep learning-based approaches for robust classification of cervical cancer. Neural Comput & Applic 35, 18813–18828 (2023). https://doi.org/10.1007/s00521-023-08757-w

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