Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Kalbhor, Madhura; Shinde, Swati Vijay; Jude, Hemant

doi:10.12928/telkomnika.v20i5.22440

Cited by 12 publications

(11 citation statements)

References 28 publications

(32 reference statements)

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“…Medical diagnosis has been upgraded by different tools and techniques like surgical analysis [ 31 ], EEG encoding [ 32 ], CT imaging [ 33 , 34 ], and surgical navigation [ 35 ]. The discrete wavelet and cosine transform were used by Kalbhor and colleagues in their research study [ 36 ] to extract characteristics. They used the fractional coefficient technique to effectively decrease the dimensionality of these characteristics.…”

Section: Related Workmentioning

confidence: 99%

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Munshi

2024

PLoS ONE

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Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models’ efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model’s capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study’s findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention.

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Section: Related Workmentioning

confidence: 99%

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Munshi

2024

PLoS ONE

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“…However, all the CNN‐based models face challenges with limited receptive fields and lacks effective mechanisms for handling positional information within images 69 ; utilized an ensemble deep belief network to attain a higher accuracy of 97.20% and 99.00%, respectively, on Sipakmed and Herlev dataset, respectively. It is also observed that fine‐tuning the GoogleNet architecture 71 has resulted in higher accuracy compared to the proposed method, however, a more comprehensive and robust assessment of how well the model is likely to generalize to new and unseen data is not demonstrated.…”

Section: Resultsmentioning

confidence: 95%

CerviFormer: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

Deo,

Pal,

Panigrahi

et al. 2024

Int J Imaging Syst Tech

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Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross‐attention‐based Transfomer approach for the reliable classification of cervical cancer in pap smear images. In this study, we propose the CerviFormer‐a model that depends on the Transformers and thereby requires minimal architectural assumptions about the size of the input data. The model uses a cross‐attention technique to repeatedly consolidate the input data into a compact latent Transformer module, which enables it to manage very large‐scale inputs. We evaluated our model on two publicly available pap smear datasets. For 3‐state classification on the Sipakmed data, the model achieved an accuracy of 96.67%. For 2‐state classification on the Herlev data, the model achieved an accuracy of 94.57%. Experimental results on two publicly accessible datasets demonstrate that the proposed method achieves competitive results when compared to contemporary approaches. The proposed method brings forth a comprehensive classification model to detect cervical cancer in pap smear images. This may aid medical professionals in providing better cervical cancer treatment, consequently, enhancing the overall effectiveness of the entire testing process.

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“…In the second phase, machine learning classifiers and fuzzy min-max neural network is used for the classification process [27]. [26] sity Hospital Resize 256 × 256 transform curacy was obtained with DCT…”

Section: Proposed Methodologymentioning

confidence: 99%

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min–Max Neural Network for Cervical Cancer Diagnosis

et al. 2023

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Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min–max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min–max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min–max neural network classifiers mentioned in the literature.

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Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Cited by 12 publications

References 28 publications

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

CerviFormer: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min–Max Neural Network for Cervical Cancer Diagnosis

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