Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Venkatesan, Chandran; Sumithra, M. G.; Karthick, Alagar; George, Tony; Deivakani, M.; Balan, Elakkiya; Subramaniam, Umashankar; Manoharan, S.

doi:10.1155/2021/5584004

Cited by 120 publications

(78 citation statements)

References 44 publications

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“…The CNN + LSTM framework for personality factor classification combines CNN and LSTM to help categorize input text into various personality qualities. The integrated framework is also used to detect psychopaths and gives the best accuracy of 91.67% for classification [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

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113

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SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body’s respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient’s computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with “COVID” and “Non-COVID.” The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

113

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“…For example, the addition of cervix clinical morphology and characterization by colposcopy images or ultrasound measurements to the MTB Smart Assistant software could be quite useful in determining whether or not to employ conization in early cervical cancer. Some literature data are already available and could form the basis for a future integration project (17)(18)(19)(20).…”

Section: Discussionmentioning

confidence: 99%

Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept

et al. 2022

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AimThe first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician’s attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation.Patients and MethodData from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient’s data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed.ResultsIn the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient’s staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way.ConclusionThis is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.

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“…Several new technologies have been proposed to detect cervical cancer [ 27 , 28 , 29 , 30 ]. When comparing our proposed method to them, there were limitations of our study.…”

Section: Discussionmentioning

confidence: 99%

Tissue Characterization Using an Electrical Bioimpedance Spectroscopy-Based Multi-Electrode Probe to Screen for Cervical Intraepithelial Neoplasia

Kang

Jung

et al. 2021

Diagnostics

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The successful management of cervical intraepithelial neoplasia (CIN) with proper screening and treatment methods could prevent cervical cancer progression. We propose a bioimpedance spectroscopic measurement device and a multi-electrode probe as an independent screening tool for CIN. To evaluate the performance of this screening method, we enrolled 123 patients, including 69 patients with suspected CIN and 54 control patients without cervical dysplasia who underwent a hysterectomy for benign disease (non-CIN). Following conization, the electrical properties of the excised cervical tissue were characterized using an electrical bioimpedance spectroscopy-based multi-electrode probe. Twenty-eight multifrequency voltages were collected through the two concentric array electrodes via a sensitivity-optimized measurement protocol based on an electrical energy concentration method. The electrical properties of the CIN and non-CIN groups were compared with the results of the pathology reports. Reconstructed resistivity tended to decrease in the CIN and non-CIN groups as frequency increased. Reconstructed resistivity from 625 Hz to 50 kHz differed significantly between the CIN and non-CIN groups (p < 0.001). Using 100 kHz as the reference, the difference between the CIN and non-CIN groups was significant. Based on the difference in reconstructed resistivity between 100 kHz and the other frequencies, this method had a sensitivity of 94.3%, a specificity of 84%, and an accuracy of 90% in CIN screening. The feasibility of noninvasive CIN screening was confirmed through the difference in the frequency spectra evaluated in the excised tissue using the electrical bioimpedance spectroscopy-based multi-electrode screening probe.

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Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Cited by 120 publications

References 44 publications

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept

Tissue Characterization Using an Electrical Bioimpedance Spectroscopy-Based Multi-Electrode Probe to Screen for Cervical Intraepithelial Neoplasia

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