Detection and Identification of Cervical Cancer on Elephant Herding Optimization on Convolutional Neural Network

Kumari, Chetna; Padmashree, S.; Bhavani, R.; Priya, R.

doi:10.1109/mysurucon55714.2022.9972506

Cited by 2 publications

(5 citation statements)

References 27 publications

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“…The model is better at correctly recognizing positive cases when the true positive rate is larger [29].…”

Section: ) Sensitivity Metricmentioning

confidence: 98%

“…One such crucial instrument that assists us in assessing the effectiveness of our approach is the confusion matrix. It is a matrix of size n x n, as the name implies, where n is the number of class labels in our problem [29].…”

Section: ) Confusion Matrixmentioning

confidence: 99%

“…One of the most crucial performance indicators is accuracy, which may be calculated as the proportion of properly predicted observations to all observations, as shown below [29]:…”

Section: ) Accuracy Metricmentioning

confidence: 99%

“…Low specificity indicates that the model is mislabeling many negative findings as positive, while high specificity indicates that the model is accurately identifying the majority of the negative results [29].…”

Section: ) Sensitivity Metricmentioning

confidence: 99%

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A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

2023

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Cervical diseases, specifically cervical cancer (CC), are among the leading causes of death around the globe, imposing a significant challenge to scientists and healthcare providers dealing with cervical disease patients. None of the existing solutions can detect various cervical diseases, which would lead the experts to accurately detect the early stages of cervical diseases due to the equipment limitations and the type of medical detection tests used in those solutions. New technologies have been developed to enable more rapid and sensitive cervical cancer screening using deep learning algorithms. This study proposes a predictive model using deep learning (DL) algorithms and colposcopy images to detect different classes of cervical diseases, including different stages of cervical diseases. This offers the medical sector an opportunity for early-stage diagnosis of cervical diseases. Four rounds of experiments were conducted in this research to evaluate the performance of the proposed model. According to the results, the proposed model can detect classes (stages) of cervical diseases while it obtains high accuracy. The rate of accuracy in the training stage was above 92%, and the highest achieved accuracy was 99% in the third experiment. Also, in this round of the experiment, the model could achieve the highest performance results in accuracy, and sensitivity with values of 98% and 98%, respectively. Notably, the third and last experiments achieved a perfect specificity value of 1.

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“…The model is better at correctly recognizing positive cases when the true positive rate is larger [29].…”

Section: ) Sensitivity Metricmentioning

confidence: 98%

Section: ) Confusion Matrixmentioning

confidence: 99%

“…One of the most crucial performance indicators is accuracy, which may be calculated as the proportion of properly predicted observations to all observations, as shown below [29]:…”

Section: ) Accuracy Metricmentioning

confidence: 99%

Section: ) Sensitivity Metricmentioning

confidence: 99%

See 2 more Smart Citations

A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

2023

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“…Still on the topic of using deep-learning-based approaches to detect and classify cervical lesions, several recent works proved its feasibility to support cervical cancer screening, with proposed approaches that explored the usage of different deep convolutional neural networks ( [13,14]) and architectures, such as MobileNet [15,16], EfficientNet [15,17], as well as newly proposed networks, like the series-parallel fusion network (SPFNet) [18], Cervical Ensemble Network (CEENET) [19], or EfficientNet Fuzzy Extreme-Learning Machine (EN-FELM) [20]. Despite the promising results of these previous works, it should be noted that the vast majority do not take into account limitations like restricted computational resources to run the models.…”

Section: Related Workmentioning

confidence: 99%

Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

Mosiichuk,

Sampaio,

Viana

et al. 2023

Applied Sciences

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Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the µSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.

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Detection and Identification of Cervical Cancer on Elephant Herding Optimization on Convolutional Neural Network

Cited by 2 publications

References 27 publications

A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

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