Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier

Geetha, R. V.; Sivasubramanian, S; Kaliappan, M.; Vimal, S.; Suresh, A.

doi:10.1007/s10916-019-1402-6

Cited by 93 publications

(41 citation statements)

References 21 publications

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“…On the other hand, conventional algorithms hold their supremacy as the above techniques are not implemented in many applications outside the research study [25]. Due to the high computation time for running several matrix operations involved with the deep learning autoencoders, the technology becomes difficult to be adapted with the real-time applications.…”

Section: Proposed Methodologymentioning

confidence: 99%

RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications

Udendhran

Balamurugan

2020

Complex Intell. Syst.

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The immense growth of the cloud infrastructure leads to the deployment of several machine learning as a service (MLaaS) in which the training and the development of machine learning models are ultimately performed in the cloud providers’ environment. However, this could also cause potential security threats and privacy risk as the deep learning algorithms need to access generated data collection, which lacks security in nature. This paper predominately focuses on developing a secure deep learning system design with the threat analysis involved within the smart farming technologies as they are acquiring more attention towards the global food supply needs with their intensifying demands. Smart farming is known to be a combination of data-driven technology and agricultural applications that helps in yielding quality food products with the enhancing crop yield. Nowadays, many use cases had been developed by executing smart farming paradigm and promote high impacts on the agricultural lands.

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Section: Proposed Methodologymentioning

confidence: 99%

RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications

Udendhran

Balamurugan

2020

Complex Intell. Syst.

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“…In [7,8], the author performs a survey on attack detection methods which detects altered finger prints. As the biometrics are mostly used in overall systems for the restriction and authentication of different users, the malicious user's tries to access the system by generating fake finger prints.…”

Section: Related Workmentioning

confidence: 99%

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Baskar

Devi

Ramkumar

et al. 2021

Neural Process Lett

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The problem of forgery detection has been well studied and the forged finger prints produces highly impacting results in the biometric based security systems. There are many algorithms discussed earlier to detect forged finger prints. However, they suffer to achieve higher performance in terms of security. In this paper, a region centric minutiae propagation measure (RCMPM) based approach. First, the finger print image is read and removes the noisy points by applying the multi level Gabor filters. The Gabor filter has been applied in multiple level which helps to remove the noise from finger print image. The enhanced image is converted into number of integral image. The integral images are generated by splitting the image into number of tiny images according to the size of window. From the integral image produced, the island, dot, enclosure, bifurcation features are extracted. Extracted features are framed as feature vector and used to estimate the RCMPM measure. Based on the RCMPM measure, the presence of forged finger print has been identified and the same has been used to identify the region which has been modified. The accuracy of forged print detection has been improved and reduces the false classification ratio.

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“…Sharma [5] using data with 237 patients and 10 features (http://www.igcs.org) proposes in his work an algorithm based on a classification tree, achieving good results with the C5.0 method with accuracy of 67.5% using advance pruning option. More recent studies regarding the use of machine learning techniques for cervical cancer prevention, such as Wu and Zhou [6] and Geetha et al [7] applying, respectively, an SVM algorithm on data composed of 32 risk factors and 4 target variables, the authors also use principal component analysis (PCA) to eliminate recursion of some characteristics. The most recent work by Geetha et al [7] again using PCA they eliminate the recursion of the characteristics and balance the data sample through the SMOTE technique and then carry out the classification through a method based on decision trees known as random forest.…”

Section: Literacy Reviewmentioning

confidence: 99%

“…More recent studies regarding the use of machine learning techniques for cervical cancer prevention, such as Wu and Zhou [6] and Geetha et al [7] applying, respectively, an SVM algorithm on data composed of 32 risk factors and 4 target variables, the authors also use principal component analysis (PCA) to eliminate recursion of some characteristics. The most recent work by Geetha et al [7] again using PCA they eliminate the recursion of the characteristics and balance the data sample through the SMOTE technique and then carry out the classification through a method based on decision trees known as random forest. Starting from the work of Sobar and Wijaya [8], the authors investigate how to predict a certain type of cervical cancer in advance.…”

Section: Literacy Reviewmentioning

confidence: 99%

Cervical cancer risk prediction with robust ensemble and explainable black boxes method

Curia

2021

Health Technol.

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Clinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they suffer from the fact that often the methods used are hard to interpret and difficult to understand on how some decisions are made; the opacity of some methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also suffer from the poor possibility of being interpreted; in the clinical context therefore it is required that the methods used are as accurate as possible, transparent techniques and explainable results. In this work the problem of the development of cervical cancer is treated, a disease that mainly affects the female population. In order to introduce advanced machine learning techniques in a clinical decision support system that can be transparent and explainable, a robust, accurate ensemble method is presented, in terms of error and sensitivity linked to the classification of possible development of the aforementioned pathology and advanced techniques are also presented of explainability and interpretability (Explanaible Machine Learning) applied to the context of CDSS such as Lime and Shapley. The results obtained, as well as being interesting, are understandable and can be implemented in the treatment of this type of problem.

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Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier

Cited by 93 publications

References 21 publications

RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications

RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Cervical cancer risk prediction with robust ensemble and explainable black boxes method

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