The rest of the paper is structured as follows: In "Literature survey", we discuss the prior work done in CC prediction and other recent DL approaches. In "Proposed methodology", we explain the research methodology. "Experimentation and Results" discusses the experimental setup, evaluation metrics, and results. "Conclusion" includes the conclusion, implications, and future research.
To improve CC prediction in this area, numerous studies have been carried out using neural network techniques. Tan et al. presented that Pap smear images can be used to classify CC using deep CNN models. Without the need for segmentation or manually created features, using the Herlev dataset, this work developed DL algorithms for the automatic diagnosis of CC. Using transfer learning in Google Colab through Keras, thirteen pre-trained CNN models were assessed. DenseNet-201 achieved the highest accuracy in the seven-class classification task. The main benefit was effective performance at low computational cost. The scale of the dataset and the models' reliance on transfer learning as opposed to domain-specific feature learning, however, were the method's limitations.
Deo et al. have presented CerviFormer, a cross-attention and latent transformer-based CC classification approach based on pap smears. Using the Herlev and Sipakmed datasets, this study introduced CerviFormer, a Transformer method based on cross-attention that can be used to identify CC in Pap screening images. It effectively used a small latent Transformer module to process large-scale inputs with few architectural assumptions. Its binary classification accuracy on the Herlev dataset was 94.57%. Strong efficiency and adaptability were benefits. Nevertheless, the model's intricacy and possible requirement for substantial computer resources would restrict its application in clinical settings with limited resources.
Bhavsar et al. have presented TransPapCanCervix: An Improved Ensemble Model for Classifying CC Based on Transfer Learning. The 1,140 single-cell images from the Herlev dataset were used in this study to classify squamous cell carcinoma (SCC) using both individual and ensemble DL models, XceptionNet, DenseNet121/169, InceptionResNet, and ResNet50/101, among others. K-fold cross-validation validated the model's resilience. The approach provided accurate SCC classification with up to 98% accuracy. Improved diagnostic performance and dependability were benefits; nevertheless, high model complexity and processing demands could be disadvantages that affect real-time clinical application.
Kalbhor et al. have presented feature transfer and learning methods for CC detection using CNNs. This study used the Herlev dataset to automatically classify Pap smear cells using two DL algorithms. The accuracy of ResNet-50, which employed machine learning (ML) classifiers using models that have been trained as feature extractors, was 92.03%. The second used fine-tuning in conjunction with transfer learning, and GoogleNet achieved 96.01% accuracy. Benefits include increased diagnostic precision and flexibility with sparse data. Reliance on pre-trained models and the possibility of over-fitting while fine-tuning are drawbacks, though, necessitating rigorous validation and significant processing power.
Popescu et al. have presented ML classifiers and fuzzy min-max networks of neurons that were hybridized with DL models that have been trained for the identification of CC. This research proposed a hybrid method for Pap smear image detection that integrated ML classifiers, a fuzzy min-max neural network, and DL methods using the Herlev and Sipakmed datasets. Pre-trained models such as GoogleNet, ResNet-18/50, and AlexNet were employed for feature extraction. The Herlev dataset shows good performance from ResNet-50. The method used fuzzy logic to improve interpretability and accuracy. However, because of the integration of numerous architectures and tuning procedures, complexity and training time could be disadvantages.
Chen et al. have presented a single-center validation of a customized prognostic prediction technique for high-grade endocrine CC and an investigation of the SEER database. To forecast a patient's cancer-specific survival in CHGNEC, including SCNEC and LCNEC cases, this study created a predictive nomogram using the SEER dataset. Age, cancer stage, T1, N0, and surgery were found to be important predictive variables. Strong predictive performance was demonstrated by external validation using clinical data, with AUC values as high as 0.85. Benefits include more individualized treatment planning and better risk classification. However, the limited external validation cohort and possible inter-institution variability in clinical data were drawbacks.
Shi et al. have presented an enhanced prognosis accuracy with a SEER-based method for forecasting both general and cancer-specific mortality for individuals with cervical adenocarcinoma. This work used the SEER dataset to design and evaluate predictive nomograms that were to forecast overall survival (OS) and cancer-specific survival (CSS) for people with cervical adenocarcinoma (CA) at 1, 3, and 5 years. Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analysis were used to identify significant characteristics such as age, TNM stage, SEER stage, grade, and tumor size. Strong predictive accuracy was demonstrated by the nomograms (C-index up to 0.832). Although there were benefits, such as accurate risk classification, generalizability may be constrained by retrospective data and possible selection bias.
Xie et al. have presented the research that utilized a SEER database analysis to develop nomograms that estimated the diagnostic outcome and prognosis for women with CC combined with second primary malignancies. The research developed prognosis and diagnosis nomograms for CC patients with second primary malignancies by using SEER data from 2000 to 2019. Analysis using logistic and Cox regression identified essential clinical elements, which included patient age along with FIGO stage, as well as therapeutic approaches. The prognostic model demonstrated satisfactory accuracy when predicting 5-year overall survival rates, while the diagnostic nomogram demonstrated good accuracy with an AUC value reaching up to 0.851. The system helps to create better follow-up decisions as well as patient-specific risk evaluations, but limitations exist because of its retrospective nature and restricted population coverage.
Shan et al. have presented a SEER-based investigation of the nomogram, prognostic variables, and incidence of cervical carcinoma with lung metastases. This study built a visual nomogram for overall survival prediction at three and five years by analyzing CC lung metastasis patients from the SEER database (2010-2015) to find survival statistics and prevalence with prognostic factors. The methods used for variable selection included Cox regression and Kaplan-Meier analysis, which established grade, surgery, and treatment order as important variables. A high predictive accuracy level was shown through the nomogram's performance, which exceeded 0.93 AUC. The model contains data limitations from retrospective studies, along with underreporting of variables associated with metastasis as its main weaknesses, although it provided benefits for planning patient-specific treatments.
Tong et al. have used the SEER database to study the treatment of uterine cancer in patients who received radiation therapy for CC. The research relied on the SEER database to study the treatment patterns of uterine cancers that developed following CC treatment with radiation therapy. Both univariate and multivariate Cox regression studies showed that patients who had chemotherapy in addition to surgery had a higher overall survival rate. Proof through Kaplan-Meier analysis also demonstrated these findings. The surgery-plus-chemotherapy group had the highest results, but radiotherapy and chemotherapy by themselves only provided modest benefits. The study's shortcomings include retrospective bias and the absence of clinical variables outside the purview of SEER, even though it provides insightful recommendations for individualized care.
In recent research in medical and applied DL, attention-based models as well as heterogeneous models have been given major focus. Along with them, efficient backbones and preprocessing specific to the domain have been proven to improve both accuracy and robustness. As has been observed in long short-term memory-variational encoder (LSTM-VAE) models that perform over complex cyber-physical data, VAE-based models that use hybrid models of signal decomposition and recurrent autoencoders are strong in temporal as well as structural latent feature extraction. Sequence models together with hybrid signal processing front ends (such as Hilbert-Huang Transform (HHT) + LSTM-VAE + Bidirectional Gated Recurrent Unit (Bi-GRU)) purvey multi-domain feature extraction that lessens false positives in noisy data as well as improves sensitivity to minute abnormalities.
In the same manner, combining classical transformations with smart deep neural network (DNN) classifiers (e.g., Fast Fourier Transform (FFT) + DNN) is instrumental in distinguishing between various disturbances and intrusive patterns. This brings the importance of domain-knowledge-driven pre-processing that explicitly captures the features for subsequent deep classification, as shown in. The DCSSGA-UNet proposed in combined the DenseNet201 and attention mechanisms to improve segmentation accuracy. The DenseNet201 model forms the basis of the encoder, which uses transition blocks and dense convolutions to extract global features at multiple scales. The decoder follows the standard U-Net architecture, while the application of semantic guidance attention (SGA) and channel spatial attention (CSA) modules helps to selectively enhance important features and reduce unnecessary ones, effectively bridging the semantic gaps.
The hybrid CNN-transformer models can be explainable fused using EfficientNet/ResNet backbones and vision transformer (ViT) (EFFResNet-ViT), which not only outperforms the previous methods in terms of classification but also provides interpretability to the models with Grad-CAM. Grad CAM helps the healthcare professionals visualize the reasoning behind the model's decisions, which in turn, enhances the reliability of deployment in clinical environments.
As with many other medical imaging tasks, DL has been applied for the prediction of CC with multitask datasets such as Herlev and SEER serving as benchmark tests. While automated models report accuracies of over 95%, there are still significant gaps in methodology, interpretability, and robustness of models. Multi-CNN frameworks and hybrid classifiers attained 94-97% accuracy on Herlev images, which is similar to the performance of classical CNNs and ensemble methods on cytology datasets. Their performance, however, is negatively impacted when evaluated on external datasets and is further downgraded due to overfitting and sensitivity to staining variations.
While EFFResNet-ViT and similar models provide Grad-CAM visualization that allows clinicians to see areas that influenced decisions, such approaches are expensive to compute and need a lot of training data. There is a need for lighter explainable models that can be deployed in low-resource clinical settings. Integrating cytological images with clinical or demographic data (such as HPV type, age, and survival data) has been examined in a variety of studies. For instance, models trained on the SEER database incorporate risk factors with image-derived features for prognosis prediction. Although these approaches show promise, they typically face challenges in dealing with multi-modal data and adapting to new cohorts.
In CC datasets, class imbalance is a strongly pronounced problem with benign cases far outpacing malignant cases. Researchers have used SMOTE-based oversampling, GANs for synthetic image generation, and focal loss for imbalance handling. Although these methods improve the minority-class sensitivity, they tend to increase the computational cost or generate unrealistic samples. Recent state-of-the-art models further illustrate the advantages of integrating CNNs with transformers, attention modules, or domain-specific preprocessing techniques. These include DCSSGA-UNet for segmentation, FFT + DNN for classification in the frequency domain, and the combined signal-processing and recurrent models.
The discussed review improves feature extraction but only on certain datasets, and they are also resource-intensive. The review above illustrates the following main points:
The framework proposed, DK-D53-DWSCNNet, addresses the above-mentioned issues by adopting deformable kernels for adaptive preprocessing, using contextual attention for more accurate segmentation, employing depth-wise separable convolutions for lighter-weight feature extraction, and applying the HSO for balanced training of imbalanced datasets. The innovation lies in the targeted improvements for the limitations of earlier CNN/transformer models: enhancement in interpretability, efficiency, and -- and this is the most important aspect -- the Herlev and SEER datasets.