Computer-Aided Detection and Diagnosis of Breast Cancer: a Review
Statistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for th...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-06-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31412 |
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| author | Bhanu Prakash Sharma Ravindra Kumar Purwar |
| author_facet | Bhanu Prakash Sharma Ravindra Kumar Purwar |
| author_sort | Bhanu Prakash Sharma |
| collection | DOAJ |
| description | Statistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for this purpose. Many perform well (over 90% of classification accuracy, sensitivity, specificity, and f-1 sore), nevertheless, there is still room for improvement. This paper reviews literature related to breast cancer and the challenges faced by the research community. It discusses the common stages of breast cancer detection/ diagnosis using CAD models along with deep learning and transfer learning (TL) methods. In recent studies, deep learning models outperformed the handcrafted feature extraction and classification task and the semantic segmentation of ROI images achieved good results. An accuracy of up to 99.8% has been obtained using these techniques. Furthermore, using TL, researchers combine the power of both, pre-trained deep learning-based networks and traditional feature extraction approaches. |
| format | Article |
| id | doaj-art-b6f94c1c3fc4471fa5464dfd3461cf82 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-b6f94c1c3fc4471fa5464dfd3461cf822025-01-23T11:25:19ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-06-0113e31412e3141210.14201/adcaij.3141236890Computer-Aided Detection and Diagnosis of Breast Cancer: a ReviewBhanu Prakash Sharma0Ravindra Kumar Purwar1Guru Gobind Singh Indraprastha University, New Delhi-IndiaGuru Gobind Singh Indraprastha University, New Delhi-IndiaStatistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for this purpose. Many perform well (over 90% of classification accuracy, sensitivity, specificity, and f-1 sore), nevertheless, there is still room for improvement. This paper reviews literature related to breast cancer and the challenges faced by the research community. It discusses the common stages of breast cancer detection/ diagnosis using CAD models along with deep learning and transfer learning (TL) methods. In recent studies, deep learning models outperformed the handcrafted feature extraction and classification task and the semantic segmentation of ROI images achieved good results. An accuracy of up to 99.8% has been obtained using these techniques. Furthermore, using TL, researchers combine the power of both, pre-trained deep learning-based networks and traditional feature extraction approaches.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31412cancerbreast cancermammogramsegmentationclassification modelmachine learningdeep learningtransfer learning |
| spellingShingle | Bhanu Prakash Sharma Ravindra Kumar Purwar Computer-Aided Detection and Diagnosis of Breast Cancer: a Review Advances in Distributed Computing and Artificial Intelligence Journal cancer breast cancer mammogram segmentation classification model machine learning deep learning transfer learning |
| title | Computer-Aided Detection and Diagnosis of Breast Cancer: a Review |
| title_full | Computer-Aided Detection and Diagnosis of Breast Cancer: a Review |
| title_fullStr | Computer-Aided Detection and Diagnosis of Breast Cancer: a Review |
| title_full_unstemmed | Computer-Aided Detection and Diagnosis of Breast Cancer: a Review |
| title_short | Computer-Aided Detection and Diagnosis of Breast Cancer: a Review |
| title_sort | computer aided detection and diagnosis of breast cancer a review |
| topic | cancer breast cancer mammogram segmentation classification model machine learning deep learning transfer learning |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31412 |
| work_keys_str_mv | AT bhanuprakashsharma computeraideddetectionanddiagnosisofbreastcancerareview AT ravindrakumarpurwar computeraideddetectionanddiagnosisofbreastcancerareview |