| Type of Material: | Thesis |
| Title: | A Novel Approach For Breast Cancer Detection Using Neural Networks |
| Researcher: | PRASATH ALIAS SURENDHAR S. |
| Guide: | VASUKI R. |
| Department: | Department of Biomedical Engineering |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2021 |
| Language: | English |
| Subject: | Engineering | Engineering and Technology | Engineering Biomedical | Biomedical Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Biomedical Engineering, Bharath University, Chennai, Chennai; 2021; D16BM502 |
| Fulltext: | Shodhganga |
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| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aPRASATH ALIAS SURENDHAR S.|eResearcher |
| 110 | __ | |aDepartment of Biomedical Engineering|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aA Novel Approach For Breast Cancer Detection Using Neural Networks |
| 260 | __ | |bBharath University, Chennai|aChennai|c2021 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Biomedical Engineering, Bharath University, Chennai, Chennai|d2021|oD16BM502 |
| 520 | __ | |aIn the breast tissue, growth of malignant cells started from breast lobules or milk ducts inner lining is known as Breast cancer. This malignant growth will spread to other organs. Most widely spread cancer in women is breast and it takes second place among widespread disease across the world. Thus, it is vital for checking the number of lives lost because of premature breast cancer in positive management and the drop. The fast development in machine learning and exclusively deep learning endures the interest of medical imaging society in spread over these methods to enhance the accuracy of breast cancer diagnosis, Machine-learning approaches, with a focus on deep learning algorithms, have particularly shown a promising applicability in medical image analysis in the area of nuclear medicine. But, breast cancer s accuracy of classification is evaluated by the examination of approaches in machine-learning, linking Convolution Neural Networks (CNN) systems, was not still established. In view of this, the entir |
| 650 | __ | |aBiomedical Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aEngineering |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aEngineering Biomedical |
| 700 | __ | |aVASUKI R.|eGuide |
| 856 | __ | |yShodhganga|uhttp://shodhganga.inflibnet.ac.in/handle/10603/341004 |
| 905 | __ | |afromsg |
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