Gene expression based prediction of prognostic outcome in ovarian cancer

Ahn, Tae-Jin; Kang, Nayeon; Kim, Yonggab; Park, Taesung

doi:10.1109/bibm.2018.8621205

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2018

DOI: 10.1109/bibm.2018.8621205

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Gene expression based prediction of prognostic outcome in ovarian cancer

Tae-Jin Ahn

Nayeon Kang

Yonggab Kim

et al.

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2021

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“…In 2018, Ahn et al [14] submitted a calculation that indicates the impact of the genetic factors in ovarian cancer. A large amount of impactful information has been given by genetic factors.…”

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confidence: 99%

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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There are many research studies in the field of breast cancer prediction, but it has been observed that the time taken for prediction needs to be reduced. The problem in the existing research is space consumption by graphical content. The proposed research is supposed to minimize the prediction time and space consumption. In this paper, research has focused on the study of existing breast cancer research and techniques and eliminating their limitation. It has been observed that when the number of datasets increases, every comparison makes a huge gap in size and comparison time. This research proposes a methodology for breast cancer prediction using an edge-based CNN (convolutional neural network) algorithm. The elimination of useless content from the graphical image before applying CNN has reduced the time consumption along with space consumption. The edge detection mechanism would retail only edges from the image sample in order to detect the pattern to predict breast cancer. The proposed work is supposed to implement the proposed methodology. A comparison of the proposed methodology and algorithm with the existing algorithm is made during simulation. The proposed work is found to be more efficient compared to the existing techniques used in breast cancer prediction. The utilization of proposed in the work area of medical science is supposed to enhance the capability in case of CNN at the time of decision-making. The proposed work is supposed to be more accurate compared to the existing works. It has been observed that the proposed work is fourteen to fifteen percent more accurate. It is taking 9/4 times less space and 1.0849004/0.178971 times less time compared to the general CNN model. Accuracy might vary as per size of the image and alteration performed in dataset of the image.

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confidence: 99%

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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Gene expression based prediction of prognostic outcome in ovarian cancer

Cited by 1 publication

References 8 publications

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

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