Breast Cancer Prediction Using Transfer Learning-Based Classification Model
Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep l...
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| Format: | Article |
| Language: | English |
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Ital Publication
2024-12-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2428 |
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| author | Sheeba Armoogum Kezhilen Motean Deshinta Arrova Dewi Tri Basuki Kurniawan Jureerat Kijsomporn |
| author_facet | Sheeba Armoogum Kezhilen Motean Deshinta Arrova Dewi Tri Basuki Kurniawan Jureerat Kijsomporn |
| author_sort | Sheeba Armoogum |
| collection | DOAJ |
| description | Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics.
Doi: 10.28991/ESJ-2024-08-06-014
Full Text: PDF |
| format | Article |
| id | doaj-art-0f8e09b3925c4ccf98240242976711e6 |
| institution | Kabale University |
| issn | 2610-9182 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-0f8e09b3925c4ccf98240242976711e62024-12-07T14:11:58ZengItal PublicationEmerging Science Journal2610-91822024-12-01862373238410.28991/ESJ-2024-08-06-014745Breast Cancer Prediction Using Transfer Learning-Based Classification ModelSheeba Armoogum0Kezhilen Motean1Deshinta Arrova Dewi2Tri Basuki Kurniawan3Jureerat Kijsomporn4Faculty of Information, Communication and Digital Technologies, University of Mauritius, Moka,Spoon Consulting, Moka,INTI International University, Nilai,Faculty of Science and Technology, Universitas Bina Darma Palembang, 30264, UBD, Palembang,Nursing Faculty, Shinawatra University, Bang Toei,Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics. Doi: 10.28991/ESJ-2024-08-06-014 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2428breast cancermachine learningdeep learningtransfer learningcnnbusiprocess innovationpublic health. |
| spellingShingle | Sheeba Armoogum Kezhilen Motean Deshinta Arrova Dewi Tri Basuki Kurniawan Jureerat Kijsomporn Breast Cancer Prediction Using Transfer Learning-Based Classification Model Emerging Science Journal breast cancer machine learning deep learning transfer learning cnn busi process innovation public health. |
| title | Breast Cancer Prediction Using Transfer Learning-Based Classification Model |
| title_full | Breast Cancer Prediction Using Transfer Learning-Based Classification Model |
| title_fullStr | Breast Cancer Prediction Using Transfer Learning-Based Classification Model |
| title_full_unstemmed | Breast Cancer Prediction Using Transfer Learning-Based Classification Model |
| title_short | Breast Cancer Prediction Using Transfer Learning-Based Classification Model |
| title_sort | breast cancer prediction using transfer learning based classification model |
| topic | breast cancer machine learning deep learning transfer learning cnn busi process innovation public health. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2428 |
| work_keys_str_mv | AT sheebaarmoogum breastcancerpredictionusingtransferlearningbasedclassificationmodel AT kezhilenmotean breastcancerpredictionusingtransferlearningbasedclassificationmodel AT deshintaarrovadewi breastcancerpredictionusingtransferlearningbasedclassificationmodel AT tribasukikurniawan breastcancerpredictionusingtransferlearningbasedclassificationmodel AT jureeratkijsomporn breastcancerpredictionusingtransferlearningbasedclassificationmodel |