Cervical cancer is the fourth most common disease and a significant concern for women’s health worldwide. This systematic review aimed to explore the segmentation and classification of Pap smear images for cervical cancer detection using deep learning techniques in articles published between 2016 and 2023. The Pap smear test was used to screen the internal cervical organs for abnormal cells. Researchers have developed various advanced image processing approaches, feature extraction methods, and classification algorithms to detect cervical cancer by characterizing the contents of Pap-smeared images. This review aimed to provide a comprehensive assessment of the most recent techniques in the scientific field by presenting a review of published articles related to cervical smear screening. The two key focal points of this review are the cell image segmentation and classification algorithms for categorizing Pap smear images for cervical cancer detection. Among the selected studies, Unet and Mask RCNN stand out as the most widely used techniques for segmenting Pap smear images, while SVM and CNN are used to classify their levels based on the extracted features. Most of the algorithms achieved more than 90% accuracy on the Pap smear images. For binary classification, accuracies of 98.32% and 98.86% were achieved for the Herlev and SIPaKMeD datasets, respectively, using a hybrid feature fusion technique. The effective utilization of deep and machine learning techniques may have the potential to enhance the accuracy and efficiency of cervical cancer detection in early diagnosis.

Cervical cancer is a type of cancer in which abnormal cell growth occurs on the surface lining of the cervix. In this study, we propose a novel residual deep convolutional generative adversarial network (RES_DCGAN) for data augmentation and ResNet50V2 self-attention method to classify cervical cells, to improve the generalizability and performance of the model. The proposed method involves adding residual blocks in the generator of the DCGAN to enhance data flow and generate higher-quality images. Subsequently, a self-attention mechanism is incorporated at the top of the pre-trained models to allow the model to focus more on significant features of the input data. To evaluate our approach, we utilized the Pomeranian and SIPaKMeD cervical cell imaging datasets. The results demonstrate superior performance, achieving an accuracy of 98% with Xception and 96.4% with ResNet50V2 on the Pomeranian dataset. Additionally, DenseNet121 with self-attention achieved accuracies of 92% and 95% in multiclass and binary classification, respectively, using the SIPaKMeD dataset. In conclusion, our RES_DCGAN-based data augmentation and pre-trained with self-attention model yields a promising result in the classification of cervical cancer cells.

Cervical cancer remains a major cause of morbidity and mortality worldwide, highlighting the need for early detection and effective treatment strategies. This study develops a robust deep learning-based system for cervical cell segmentation and classification using a novel Squeeze-and-Excitation DeepLabV3+ (SE-DeepLabV3+) model with a dynamic atrous rate for segmentation (instead of a fixed dilation rate) and an ensemble method for classification. The system leverages both global context and local features to enhance segmentation performance. The classification model utilizes an ensemble approach by combining features from multiple pre-trained convolutional neural networks (CNNs) and introducing learnable weights (dense layers applied to the global average pooling layer) to dynamically adjust the importance of each model’s feature and improve prediction accuracy. The performance of the proposed system is evaluated using the Pomeranian, SIPaKMeD, and Herlev datasets. For segmentation, the SE-based DeepLabV3+ outperforms the standard DeepLabV3+ across all datasets, achieving the highest accuracy in the Pomeranian dataset (99.09%), SIPaKMeD dataset (96.09%), and Herlev dataset (87.12%). In classification, the proposed ensemble method achieves an accuracy of 99% for the Pomeranian dataset, 94% for SIPaKMeD (multiclass), 99% for SIPaKMeD (binary), and 90% for Herlev in classification-only. These results demonstrate the effectiveness of the proposed SE-DeepLabV3+ model for segmentation and the ensemble classification method in improving overall cervical cell analysis, leading to more accurate and reliable predictions.

This study presents a novel approach to the road traffic prediction using single multilayer perceptrons and their ensemble. Networks were trained on the basis of real‐world data from the intelligent transportation system Wroclaw. This system is installed in one of the largest cities in Poland. First, a number of neural networks were created, each of which was concurrently able to predict the state of traffic on a number of major intersections located in different parts of the city. Then the multilayer perceptrons were made, which predict the numbers of vehicles passing through selected intersections using the information about previous situations at other intersections. Furthermore, an ensemble method, which combine output values of multiple neural networks, were applied. In the worst case, mean absolute percentage error did not exceed 12.6%, even in cases when traffic prediction was based only on information from other intersections.

Cervical cancer is the fourth leading cause of cancer-related illness and death among women worldwide. The Pap test is a widely used screening technique to detect abnormal cells that can become cancerous. In this research, we proposed a method for automatic segmentation and classification of cervical cancer cell images. The method uses an improved U-Net architecture to segment the image and identify the region of interest (ROI). Following segmentation, we classify the cervical cell type using ResNet50V2 and an ensemble of different pretrained models to enhance performance. We developed several pipelines for cervical cancer detection, including a normal method, with and without RES_DCGAN, before classification and segmentation tasks. The proposed method was evaluated using the Pomeranian, Herlev, and SIPaKMeD datasets. The experimental results showed that whole-cell segmentation achieved 99.53%, 88.95%, and 98.3% accuracy when RES_DCGAN was added before the segmentation. The framework achieved 96% and 95% accuracy for multi-class classification on the Pomeranian and SIPaKMeD datasets, respectively. Additionally, the Herlev dataset scored an accuracy of 91%, while SIPaKMeD achieved 99% for the binary classification of cervical cell types using the ensemble method. In conclusion, the deep learning-based segmentation and classification method demonstrated promising results for cervical cancer detection and can help pathologists diagnose the disease.