Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Abukhalil, Tamer; Alqaralleh, Bassam A. Y.; Alomari, Ahmad

doi:10.32604/cmc.2022.024367

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Several studies reported high performance measures, with ResNet-v2 used by AbuKhalil et al. ( 41 ) achieving 96.7% precision, 97.39% sensitivity, and 96.61% accuracy on 918 images. Bhatt et al.…”

Section: Resultsmentioning

confidence: 99%

“…In the literature reviewed for gynecologic cancer screening and diagnosis, various deep learning models were employed, including ResNet50, Colponet, ResNeSt, N-Net, 3D-UNet, and YOLOv3. Several studies reported high performance measures, with ResNet-v2 used by AbuKhalil et al (41) achieving 96.7% precision, 97.39% sensitivity, and 96.61% accuracy on 918 images. Bhatt et al (35) utilized convNet with transfer learning and progressive resizing with K-Nearest Neighbour and EfficientNet-B3 on 917 and 966 images, respectively, achieving 78.14% and 99.01% accuracy (Table 3 for details).…”

Section: Deep Learning Models Used For Automatic Image Classificationmentioning

confidence: 99%

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Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Taddese,

Tilahun,

Awoke

et al. 2024

Front. Oncol.

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IntroductionGynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.MethodsThe study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.ResultsThe review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias.ConclusionThis review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Deep Learning Models Used For Automatic Image Classificationmentioning

confidence: 99%

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Taddese,

Tilahun,

Awoke

et al. 2024

Front. Oncol.

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“…The CSO algorithm is used in the feature extraction stage of predicting cervical cancer (Hodson 2022) in the literature (Khan et al 2019) and(AbuKhalil et al 2022). The CSO algorithm's chickens include the feature values, which are then calculated iteratively to produce the chicken values.…”

Section: Predictionmentioning

confidence: 99%

A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. Swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.

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“…For the combination of image processing and AI, most recent studies, such as AbuKhalil, T., et al [117], enhanced Pap smear images using median filters and then segmented them using Outs thresholding techniques. The deep descriptors are extracted using ResNet and Inception modules.…”

Section: -2022mentioning

confidence: 99%

Cervical Cancer Detection Techniques: A Chronological Review

et al. 2023

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Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.

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Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Cited by 4 publications

References 20 publications

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis

A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

Cervical Cancer Detection Techniques: A Chronological Review

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