Computer Science, Computer Networks and Communications, Information Systems, Computer Science Applications
2
Scopus Publications
13
Scholar Citations
2
Scholar h-index
Scopus Publications
A Review of Deep Learning Models for Early Detection and Diagnosis of Ovarian Cancer Savitha D, Rajakumari D International Research Journal of Multidisciplinary Technovation, 2025 Ovarian cancer ranks seventh worldwide and is the third most common type of cancer diagnosed in women in India. Numerous studies have demonstrated that the number of people affected by ovarian cancer is expected to rise significantly in the future. Proactive measures for early cancer detection are essential to prevent death and recurrence. This paper attempts to review the various deep learning (DL) models in ovarian cancer diagnosis, including detecting risk factors, analyzing genomic data sets, predicting disease progression, recurrence, and mortality rates, and identifying correlations and patterns. The patient's electronic health records contain effective analytics on imaging and other types of data that may open the door to more accurate or early identification of ovarian cancer. The taxonomy of the several ways that DL aids in the diagnosis, early detection, and treatment of ovarian cancer will be compiled in this review article. As per the reviews, more research studies have examined the Convolutional Neural Networks (CNNs) approach for the Early Detection and Diagnosis of Ovarian Cancer. This is because CNNs are a popular and potent architecture for image classification tasks because of their capacity to learn spatial and hierarchical features from images effectively. The review article seeks to give future research topics and assess the state-of-the-art application of DL algorithms for ovarian cancer diagnosis.
An ovarian cancer prediction using an optimized Elman neural network based on elephant herding optimization D. Rajakumari, D. Savitha 1st International Conference on Emerging Research in Computational Science Icercs 2023 Proceedings, 2023 One of the leading causes of death for women is ovarian cancer (OC). More recently, deep learning has demonstrated improved accuracy in OC stage and subtype prediction. Nonetheless, the majority of cutting-edge deep learning models might lead to low performance due to inappropriate selection of hyperparameters. Furthermore, the optimization of the model construction—which necessitates a significant computational cost for training and deployment—remains absent from these deep learning models. This paper proposes a novel optimized technique using an optimized Elman recurrent neural network (ERNN) based on elephant herding optimization (EHO) called ERNN-EHO for predicting ovarian cancer. A variety of evaluation metrics were employed to contrast the suggested model with alternative prediction models. This was carried out utilizing an OC benchmark that was gathered from the Kaggle website. The experimental findings show that the suggested model can diagnose OC and other malignancies with greater precision and accuracy.
RECENT SCHOLAR PUBLICATIONS
A Review of Deep Learning Models for Early Detection and Diagnosis of Ovarian Cancer D Savitha, D Rajakumari International Research Journal of Multidisciplinary Technovation 7 (1), 123-137 , 2025 2025.0 Citations: 4
An ovarian cancer prediction using an optimized Elman neural network based on elephant herding optimization D Rajakumari, D Savitha 2023 International Conference on Emerging Research in Computational Science … , 2023 2023.0 Citations: 2
The internet of Smart Clothing: A Review on Application of IoT in Manufacturing Smart Textile and Clothing P Ramya, T Parimalam, D Rajakumari, S Karthika Design Engineering, 569-579 , 2021 2021.0
Pearsoncorrelationcoefficient k-nearestneighboroutlierclassification on real-time datasets D Rajakumari ICTACT Journal on Soft Computing 10, 2045-2053 , 2020 2020.0 Citations: 4
Distance Based Algorithm for Effective Outliers Classification and Prediction of WDBC Dataset D Rajakumari INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH 6 (3), 11-16-11-16 , 2018 2018.0
PERFORMANCE VALIDATION OF PRIOR QUANTIZATION TECHNIQUES IN OUTLIERS CLASSIFICATION USING WDBC DATASET D Rajakumari INTERNATIONAL JOURNAL OF ENGINEERING 5 (4), 48-56 , 2018 2018.0
Comparative analysis on Boundary-based Classification Techniques for Outlier Detection in WDBC Datasets [J] D Rajakumari, SA Pannirselvam INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING 6 (1 … , 2016 2016.0 Citations: 1
An Efficient Classification using Machine Learning Approach on Breast Cancer Datasets D Rajakumari IJCSIS , 2015 2015.0 Citations: 1
AWARENESS ON CLASS ROOM TECHNOLOGIES-EVIDENCE FROM SCHOOL TEACHERS P MOHANRAJ, D RAJAKUMARI
INTERNET OF THINGS (IOT)-APPLICATIONS IN ALL SCIENCES D Kavitha, D Rajakumari, MKC Raja Applications of IOT in Science and Technology, 222 , 0
Effective Algorithm for Distance Based Outliers Detection in WDBC Dataset D RAJAKUMARI, C JAYANTHI
A Weighing Based Feature Selection and Monotonic Classification (WFSMC) for the Effective Prediction of Outliers D Rajakumari IJCTA 6 (4), 586-593 , 0 Citations: 1
INNOVATIVE EMERGING TECHNOLOGICAL APPLICATIONS FOR TEACHING AND LEARNING METHOD D RAJAKUMARI, C JAYANTHI, KS SUDHA
MOST CITED SCHOLAR PUBLICATIONS
A Review of Deep Learning Models for Early Detection and Diagnosis of Ovarian Cancer D Savitha, D Rajakumari International Research Journal of Multidisciplinary Technovation 7 (1), 123-137 , 2025 2025.0 Citations: 4
Pearsoncorrelationcoefficient k-nearestneighboroutlierclassification on real-time datasets D Rajakumari ICTACT Journal on Soft Computing 10, 2045-2053 , 2020 2020.0 Citations: 4
An ovarian cancer prediction using an optimized Elman neural network based on elephant herding optimization D Rajakumari, D Savitha 2023 International Conference on Emerging Research in Computational Science … , 2023 2023.0 Citations: 2
Comparative analysis on Boundary-based Classification Techniques for Outlier Detection in WDBC Datasets [J] D Rajakumari, SA Pannirselvam INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING 6 (1 … , 2016 2016.0 Citations: 1
An Efficient Classification using Machine Learning Approach on Breast Cancer Datasets D Rajakumari IJCSIS , 2015 2015.0 Citations: 1
A Weighing Based Feature Selection and Monotonic Classification (WFSMC) for the Effective Prediction of Outliers D Rajakumari IJCTA 6 (4), 586-593 , 0 Citations: 1
The internet of Smart Clothing: A Review on Application of IoT in Manufacturing Smart Textile and Clothing P Ramya, T Parimalam, D Rajakumari, S Karthika Design Engineering, 569-579 , 2021 2021.0
Distance Based Algorithm for Effective Outliers Classification and Prediction of WDBC Dataset D Rajakumari INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH 6 (3), 11-16-11-16 , 2018 2018.0
PERFORMANCE VALIDATION OF PRIOR QUANTIZATION TECHNIQUES IN OUTLIERS CLASSIFICATION USING WDBC DATASET D Rajakumari INTERNATIONAL JOURNAL OF ENGINEERING 5 (4), 48-56 , 2018 2018.0
AWARENESS ON CLASS ROOM TECHNOLOGIES-EVIDENCE FROM SCHOOL TEACHERS P MOHANRAJ, D RAJAKUMARI
INTERNET OF THINGS (IOT)-APPLICATIONS IN ALL SCIENCES D Kavitha, D Rajakumari, MKC Raja Applications of IOT in Science and Technology, 222 , 0
Effective Algorithm for Distance Based Outliers Detection in WDBC Dataset D RAJAKUMARI, C JAYANTHI
INNOVATIVE EMERGING TECHNOLOGICAL APPLICATIONS FOR TEACHING AND LEARNING METHOD D RAJAKUMARI, C JAYANTHI, KS SUDHA
