Cervical cell classification based on the CART feature selection algorithm

Dong, Na; Zhai, Meng-die; Li, Zhao; Wu, Chun-Ho

doi:10.1007/s12652-020-02256-9

Cited by 15 publications

(5 citation statements)

References 39 publications

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“…Mamunur et al [ 38 ] proposed DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL to classify the cervical cells accurately; Dounias et al [ 12 ] compared the performance of various intelligent methodologies in the task of pap-smear diagnosis; Marinakis et al [ 39 ] proposed an effective genetic algorithm scheme which is combined with a number of nearest neighbor based classifiers. ; Dong et al [ 10 ] proposed a machine learning method based on feature selection algorithm for cervical cell classification.…”

Section: Discussionmentioning

confidence: 99%

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Zhao

Shuai

et al. 2022

Multimed Tools Appl

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Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes’ samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time. Supplementary Information The online version contains supplementary material available at 10.1007/s11042-022-12670-0.

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Section: Discussionmentioning

confidence: 99%

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Zhao

Shuai

et al. 2022

Multimed Tools Appl

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“…However, the postpruning method is cumbersome. Te commonly used postpruning algorithms include (1) cost complexity pruning (CCP), (2) reduce error pruning (REP), (3) minimum error pruning (MEP), and (4) pessimistic error pruning (PEP) [17]. [18].…”

Section: Decision Treementioning

confidence: 99%

Developing Countermeasures of Integrating Entrepreneurship Education with Professional Education in Colleges and Universities Using Data Mining

Deng

2022

Journal of Electrical and Computer Engineering

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The purpose is to promote the integrated development of innovation and entrepreneurship education (IEE) and professional education (PE) in colleges and universities (CAUs) more accurately and efficiently. Data mining (DM) technology is introduced to study IEE + PE in CAUs. At present, although the state strongly supports the IEE + PE, the integration progress is still slow, and IEE and PE are still completely independent of each other. Therefore, it is urgent to combine IEE with PE organically. Accordingly, based on DM technology, this work analyzes the current situation and existing problems of IEE + PE and puts forward suggestions and corresponding countermeasures for the future development of IEE + PE. The results show the following: (1) College students have high expectations of cultivating their innovation and entrepreneurship ability. (2) The internal and external influencing factors of IEE must be merged. (3) The implementation methods of IEE should be diversified. (4) The current resource allocation of IEE is unreasonable. Based on the above analysis, the existing problems are studied in detail and the corresponding countermeasures are put forward. Meanwhile, DM technology provides a theoretical data basis, making the research results more authentic and reliable and providing reference suggestions for IEE + PE.

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“…The CART algorithm identi ed three speci c groups at risk of underusing mental health care services, providing valuable insights for targeted interventions. Dong et al, (2021) investigate the ine ciencies associated with conventional arti cial methods in the classi cation of cervical cells, which often necessitate professional intervention. Recognizing the increasing reliance on arti cial intelligence for this task, the study aims to enhance classi cation e ciency.…”

Section: Mental Health Predictions and Understandingsmentioning

confidence: 99%

Transformative Analysis of Mental Health in Rural China: The CART Algorithm Approach

Fu,

He,

2024

Preprint

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Mental health remains a critical concern in China, particularly for patients with severe psychiatric disorders in rural areas. This study aimed to analyze the health status of Chinese patients with severe psychiatric disorders using the CART algorithm. Mental health is a critical facet of overall well-being, yet understanding and addressing the complexities of severe psychiatric disorders in rural China presents unique challenges. This comprehensive study employs advanced analytical techniques to explore and illuminate multifaceted aspects of mental health, with a specific focus on patients with severe psychiatric disorders, healthcare professionals, and rural residents in China. This study's novelty is in its creative utilization of the CART algorithm to assess the well-being of Chinese individuals grappling with severe psychiatric conditions. This cutting-edge data analysis method opens up a potential path for enhancing mental health strategies and optimizing resource allocation. In essence, this study offers a holistic examination of mental health in rural China, encompassing various dimensions, from predictive elements to the challenges faced by healthcare professionals. Its findings aim to inform the development of effective mental health strategies and resource allocation, enhancing the overall well-being of individuals grappling with severe psychiatric disorders in this region. The government and relevant authorities are recommended to ensure their physical and mental health. The lack of mental health information in rural China also negatively impacts patients' behavior in seeking and using medical services. Thus, measures to promote different forms of mental health education are proposed. In conclusion, the treatment of patients with severe mental illness is crucial to the physical and mental health of millions of people.

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Cervical cell classification based on the CART feature selection algorithm

Cited by 15 publications

References 39 publications

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Developing Countermeasures of Integrating Entrepreneurship Education with Professional Education in Colleges and Universities Using Data Mining

Transformative Analysis of Mental Health in Rural China: The CART Algorithm Approach

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