Machine Learning Assisted Cervical Cancer Detection

Mehmood, Mavra; Rizwan, Muhammad; ml., Michal Greguš; Abbas, Sidra

doi:10.3389/fpubh.2021.788376

Cited by 61 publications

(43 citation statements)

References 42 publications

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“…These technical models, combined with other data sets or with clinical data, have greatly expanded the scope of application. For example, various Feature Selection Technique (FST) methods were applied to the transformed datasets to predict cervical cancer or identify important risk factors [ 75 , 76 ]. The machine learning algorithm is fused with an optoelectronic sensor to realise rapid sample measurement and the automatic classification of results [ 77 ].…”

Section: Detection Of Premalignancy and Malignancy Of The Uterine Cervixmentioning

confidence: 99%

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Boon

Luk

Xiao

et al. 2022

Cancers

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Cancer arising from the uterine cervix is the fourth most common cause of cancer death among women worldwide. Almost 90% of cervical cancer mortality has occurred in low- and middle-income countries. One of the major aetiologies contributing to cervical cancer is the persistent infection by the cancer-causing types of the human papillomavirus. The disease is preventable if the premalignant lesion is detected early and managed effectively. In this review, we outlined the standard guidelines that have been introduced and implemented worldwide for decades, including the cytology, the HPV detection and genotyping, and the immunostaining of surrogate markers. In addition, the staging system used to classify the premalignancy and malignancy of the uterine cervix, as well as the safety and efficacy of the various treatment modalities in clinical trials for cervical cancers, are also discussed. In this millennial world, the advancements in computer-aided technology, including robotic modules and artificial intelligence (AI), are also incorporated into the screening, diagnostic, and treatment platforms. These innovations reduce the dependence on specialists and technologists, as well as the work burden and time incurred for sample processing. However, concerns over the practicality of these advancements remain, due to the high cost, lack of flexibility, and the judgment of a trained professional that is currently not replaceable by a machine.

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Section: Detection Of Premalignancy and Malignancy Of The Uterine Cervixmentioning

confidence: 99%

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Boon

Luk

Xiao

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…Mohiyuddin et al [ 27 ] used the YOLOv5 network to predict breast tumor with the help of a publicly available dataset of curated imaging subset of DDSM [ 28 ], and they used augmented techniques and split data 60% and 30% of training and validation, respectively, and achieved 96.50% prediction accuracy. Mehmood et al [ 29 ] used a random forest feature selection technique to predict cervical cancer with the help of a sallow neural network and achieved 93.6% prediction accuracy and 0.07111 mean squared error. Abbas et al [ 30 ] used a proposed model of breast cancer detection that utilized an extremely randomized tree and whale optimization algorithm with the help of a publicly dataset available on Kaggle and achieved a 0.99 F1-score and 0.98 recall.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Prediction Empowered with Fine-Tuning

Nasir

Ghazal

Khan

et al. 2022

Computational Intelligence and Neuroscience

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In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women’s and making it the most widespread cancer, and it is the second major reason for women’s death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.

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“…The suggested work attempts to increase the efficiency, accuracy, and speed of the process. This may be expanded in applications including information storage, picture recognition, and pattern matching [ 9 , 39 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…An expert system might use this information to determine whether or not a patient is suffering from illness. The doctor makes medical decisions based on accurate signs or measurements [ 8 , 9 ]. There has been great growth in today's contemporary period, for example, in the use of information technology and the Internet of Things (IoT) in diagnostic medicine, sickness prevention, and patient satisfaction [ 10 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

Shehzadi

Hassan

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.

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Machine Learning Assisted Cervical Cancer Detection

Cited by 61 publications

References 42 publications

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Breast Cancer Prediction Empowered with Fine-Tuning

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

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