Cervical Cancer Prevention, Diagnosis, and Management Within a Low-Resource Environment

Lellé, Ralph J.; Küppers, Volkmar

doi:10.1007/978-3-030-85388-4_17

Cited by 1 publication

(1 citation statement)

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“…For instance, Pap screening tests require extensive microscopic examinations, which are not only costly but also timeconsuming. Consequently, there is an urgent need to develop an advanced and reliable model capable of providing accurate decision-making in cervical cancer diagnosis Lellée and Küppers ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification

Alohali,

El-Rashidy,

Alaklabi

et al. 2024

Front. Oncol.

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Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.

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Section: Introductionmentioning

confidence: 99%