Cervical Cancer Risk Classification Based on Deep Convolutional Neural Network

Zahras, Durrabida; Rustam, Zuherman

doi:10.1109/icaiti.2018.8686767

Cited by 25 publications

(8 citation statements)

References 6 publications

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“…The metric formula is depicted below for a few of the innovative features. Extraction of the feature is done using a co-occurrence matrix of the gray level [ 20 ]. Several texture features are available, but this study uses only four features: strength, contrast, correlation, and homogeneity.…”

Section: Methodsmentioning

confidence: 99%

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Arif

Ajesh

Shamsudheen

et al. 2022

Journal of Healthcare Engineering

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Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley’s wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard’s coefficient, spatial overlap, AVME, and FoM.

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Section: Methodsmentioning

confidence: 99%

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Arif

Ajesh

Shamsudheen

et al. 2022

Journal of Healthcare Engineering

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“…The results of deep convolutional neural network classification were comparable to the present study, with an accuracy of about 90 % for each target. 24 Asaduzzaman et al developed a system to predict the risk of cervical cancer using machine learning models including AdaBoost, Logistics Regression, Boosting (GDB) were used with the five features to identify patients with or without cancer. The classifiers showed improved performance metrics with reduced features.…”

Section: Discussionmentioning

confidence: 99%

Optimised feature selection and cervical cancer prediction using Machine learning classification

Tak¹,

Parihar²,

Singh³

et al. 2022

Scripta Medica

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Background: Screening and early detection play a key role in cervical cancer prevention. The present study predicts the outcome of various diagnostic tests used to diagnose cervical cancer using machine learning algorithms. Methods: The present study ran various cervical cancer risk factors on a machine learning (ML) classifier to predict outcomes of Hinselmann, Schiller, cytology and biopsy. The dataset is publicly available on the Machine Learning Repository website of the University of California Irvine. The imbalanced dataset was pre-processed using oversampling methods. The significantly varied features between the two levels of a response variable were used to train the machine learning classifiers on MATLAB. The classifiers used were Decision Trees, Support Vector Machine, K-Nearest Neighbours and Ensemble learning classifiers. The performance metrics of the classifiers were expressed as accuracy, the area under the receiver operator characteristic (AU-ROC) curve, sensitivity and specificity. Results: The Fine Gaussian SVM classifier was the best to classify Hinselmann, cytology and biopsy with the accuracy of 97.5 %, 62.5 % and 98 %, respectively. However, Boosted trees performed best in the classification of Schiller with 81.3 % accuracy. Conclusion: The present study selected optimised features among multiple risk factors to train various ML classifiers to predict cervical cancer.

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“…LeCun et al constructed the first CNN. CNN's major application areas include image processing and character recognition (Akkus et al, 2017;Zahras, 2018). In terms of construction, the initial layer recognizes features, however the intermediate layer recombines features to produce high-level input characteristics, followed by classification.…”

Section: Figurementioning

confidence: 99%

Deep learning techniques for cancer classification using microarray gene expression data

Gupta

Gupta²,

Shabaz³

et al. 2022

Front. Physiol.

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Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.

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Cervical Cancer Risk Classification Based on Deep Convolutional Neural Network

Cited by 25 publications

References 6 publications

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Optimised feature selection and cervical cancer prediction using Machine learning classification

Deep learning techniques for cancer classification using microarray gene expression data

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