Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence
With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is insepa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2023-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
| Subjects: | |
| Online Access: | https://www.yrpipku.com/journal/index.php/jaets/article/view/3384 |
| _version_ | 1797208960201129984 |
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| author | Arathi Chandran R I V Mary Amala Bai |
| author_facet | Arathi Chandran R I V Mary Amala Bai |
| author_sort | Arathi Chandran R I |
| collection | DOAJ |
| description |
With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is inseparably linked to mortality. Despite its importance, the significant proportion of breast cancer datasets rarely include it, which makes research into its prediction more challenging. It is still difficult to predict who will experience a recurrence and who won't, which has implications for the treatment that goes along with it. Clinicians treating breast cancer may be able to avoid ineffective overtreatment if Artificial Intelligence (AI) methods are developed that can forecast the likelihood of breast cancer recurrence. This work proposes a novel automatic breast cancer recurrence classification and prediction system incorporating novel Deep Convolutional Neural Network (DCNN) algorithm. The proposed DCNN model is deployed on Wisconsin Breast Cancer dataset for further evaluation. The role of AI in forecasting recurrence is examined in this work. The experimental results were analysed for various combination of train and validation dataset. The accuracy, precision, recall and F1-score for the proposed DCNN was calculated as 97.63 %, 98.57 %, 96.84 %, 97.89 % respectively.
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| first_indexed | 2024-03-07T14:16:56Z |
| format | Article |
| id | doaj.art-df560b7e9024488fad0f51926ca90d5e |
| institution | Directory Open Access Journal |
| issn | 2715-6087 2715-6079 |
| language | English |
| last_indexed | 2024-04-24T09:47:06Z |
| publishDate | 2023-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj.art-df560b7e9024488fad0f51926ca90d5e2024-04-14T12:07:52ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792023-12-015110.37385/jaets.v5i1.3384Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence Arathi Chandran R I0V Mary Amala Bai1Noorul Islam Center For Higher Education (NICHE)Noorul Islam Center For Higher Education (NICHE) With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is inseparably linked to mortality. Despite its importance, the significant proportion of breast cancer datasets rarely include it, which makes research into its prediction more challenging. It is still difficult to predict who will experience a recurrence and who won't, which has implications for the treatment that goes along with it. Clinicians treating breast cancer may be able to avoid ineffective overtreatment if Artificial Intelligence (AI) methods are developed that can forecast the likelihood of breast cancer recurrence. This work proposes a novel automatic breast cancer recurrence classification and prediction system incorporating novel Deep Convolutional Neural Network (DCNN) algorithm. The proposed DCNN model is deployed on Wisconsin Breast Cancer dataset for further evaluation. The role of AI in forecasting recurrence is examined in this work. The experimental results were analysed for various combination of train and validation dataset. The accuracy, precision, recall and F1-score for the proposed DCNN was calculated as 97.63 %, 98.57 %, 96.84 %, 97.89 % respectively. https://www.yrpipku.com/journal/index.php/jaets/article/view/3384Breast CancerRecurrencePredictionDeep LearningDCNNClassification |
| spellingShingle | Arathi Chandran R I V Mary Amala Bai Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence Journal of Applied Engineering and Technological Science Breast Cancer Recurrence Prediction Deep Learning DCNN Classification |
| title | Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence |
| title_full | Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence |
| title_fullStr | Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence |
| title_full_unstemmed | Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence |
| title_short | Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence |
| title_sort | optimized deep convolutional neural network for the prediction of breast cancer recurrence |
| topic | Breast Cancer Recurrence Prediction Deep Learning DCNN Classification |
| url | https://www.yrpipku.com/journal/index.php/jaets/article/view/3384 |
| work_keys_str_mv | AT arathichandranri optimizeddeepconvolutionalneuralnetworkforthepredictionofbreastcancerrecurrence AT vmaryamalabai optimizeddeepconvolutionalneuralnetworkforthepredictionofbreastcancerrecurrence |