A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network

Hussain, Elima; Mahanta, Lipi B.; Das, Chandana; Talukdar, Ratna Kanta

doi:10.1016/j.tice.2020.101347

Cited by 77 publications

(44 citation statements)

References 19 publications

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“…In recent years, with the evolution of convolutional methods, several authors have started to study their applicability for image classification. Hussain et al [ 7 ] used the convolutional neural networks AlexNet, VGGNet (VGG-16 and VGG-19), ResNet (ResNet-50 and ResNet-101), and GoogLeNet, as well as their ensemble method, to classify four cervical lesions. Lin et al [ 15 ] proposed a method based on the GoogLeNet, AlexNet, ResNet, and DenseNet convolutional neural networks that combined cell appearance and morphology to perform the classification.…”

Section: Related Workmentioning

confidence: 99%

“…With the evolution of technologies, several systems that use computational algorithms to automatically analyze cell images have been developed in order to improve screening efficiency and accuracy. Some authors, such as Silva et al [ 5 ] and Isidoro et al [ 6 ], used traditional machine learning techniques (a support vector machine and handcrafted features) to perform the cell classification, while others employed convolutional neural networks to perform the classification [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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“…To evaluate the significance of performance differences between the classification models, McNemar's statistical test continuity correction [61] that belongs to the group of Chi-squared tests was applied. In the context of machine learning models, this method is often considered when comparing the predictive accuracy of two models [62,63]. In the McNemar test, the null hypothesis (H0) can be formulated such that both models perform equally well.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Deep Learning and Conventional Machine Learning for Image-Based in-Situ Fault Detection During Laser Welding: A Comparative Study

Knaak¹,

Kröger²,

Schulze³

et al. 2021

Preprint

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An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.

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“…Without exception, these systems have increased the sensitivity of CC screening and reduced the false negative rate. However, many studies only focused on a certain part of the automatic screening diagnosis of cervical Pap smear, such as using six different CNNs to classify cervical lesions 37 , or using different algorithms to segment cervical cell and nuclei 38 and detect and classify images of PAP smears 39 . A variety of automatic screening systems are able to identify suspicious intraepithelial lesion areas 31 , 40 and allow doctors to focus on those suspicious areas.…”

Section: Introductionmentioning

confidence: 99%

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Zhu

Ong

et al. 2021

Nat Commun

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Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.

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A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network

Cited by 77 publications

References 19 publications

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

Deep Learning and Conventional Machine Learning for Image-Based in-Situ Fault Detection During Laser Welding: A Comparative Study

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

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