Prediction and Classification of Ovarian Cancer using Enhanced Deep Convolutional Neural Network

Kasture, Kokila. R.; Choudhari, Dharmaveer; Matte, Pravin N.

doi:10.14445/22315381/ijett-v70i3p235

Cited by 6 publications

(2 citation statements)

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“…Then, it uses the previously generated feature maps to be as input feature maps and applies the same process on them, it still goes on layer by layer and extract powerful feature and get smaller size feature maps. After that, the final powerful feature, reduced dimension feature maps are flattened to generate low dimensional feature vector to be fed into classifier [21].…”

Section: Feature Extractormentioning

confidence: 99%

Early Detection and Segmentation of Ovarian Tumor Using Convolutional Neural Network with Ultrasound Imaging

Alwan,

Kadhim,

Issa

et al. 2023

RIA

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The clinical differentiation between benign and malignant ovarian tumors represents a substantial challenge in the fields of obstetrics and gynecology, particularly following the detection of ovarian cysts through ultrasound. Ovarian cancers, diverse in type, often exhibit overlapping characteristics that complicate diagnosis. In this study, a deep learningbased methodology was developed to aid in the rapid and accurate differentiation of ovarian cancer types using ultrasound imaging. A deep learning approach, utilizing transfer learning with Convolutional Neural Network (CNN) models, was employed. To ensure the stability and robustness of the solution, ten iterations of training and validation were executed, with data randomly sampled for each iteration. The mean of the ten iterations' outcomes constituted the final evaluation metric. Initially, ultrasound images were enhanced to augment the quality of the training dataset, followed by the extraction of low-level texture features for the segmentation of images. Subsequently, ten established CNN models were utilized for both training and transfer learning processes. In the culmination of the study, a multitask model was proposed, capable of concurrently executing detection and segmentation tasks. The conducted evaluations reveal that the deep learning models can classify ovarian tumors with an accuracy of 98.79%, a rate comparable to that of skilled medical practitioners.

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Section: Feature Extractormentioning

confidence: 99%

Early Detection and Segmentation of Ovarian Tumor Using Convolutional Neural Network with Ultrasound Imaging

Alwan,

Kadhim,

Issa

et al. 2023

RIA

View full text Add to dashboard Cite

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“…R. Kasture et al had used the histopathological images for the prediction of ovarian cancer [9]. Deep Learning method DCNN is used for training and evaluation [10].…”

Section: Introductionmentioning

confidence: 99%

Ovarain Cancer Prediction in Early Stage Using Machine Learning Approaches

2023

ijfans

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Ovarian cancer is a disorder of ovarian cell growth that is triggered by series of acquired mutations affecting a single cell or its clonal progeny. It is purposeless prey on host and virtually autonomous. It is usually diagnosed at a late stage because of poor sensitivity of screening test. There are still no effective cures for this illness. Still early detection might lower the mortality rate. Our project's major goal is to conduct predictive analytics for early detection by using machine learning models and statistical techniques on clinical data collected from specific patients. Mutual information testing is crucial in statistical analysis for identifying indicative biomarkers. A collection of machine learning models, such as the Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM), and Light Gradient Boosting Machine (LGBM)are utilized in the classificationof ovarian tumors as benign or malignant. By using proposed system, it can significantly identify the class of benign and malignant patients. The data collected is analyzed and pre-processed before it is used for model training and testing.

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