Current Volume 8
Abstract
Breast Cancer is one of the major cancer type that is causing 2nd highest fatality rate in women all over the world. It is detected by various technical methods like MRI scans, City scans etc., and combining the results with manual identifications like lumps at breast nodes etc., But it is very difficult to detect cancer accurately by the doctor, surgeon, or a pathologist. It requires years of experience to predict better results manually.To overcome this problem, Doctors started depending on most powerful techniques like deep learning and artificial intelligence. The images of various cases were taken and are analyzed using various methods. Convolution Neural Networks which is mainly used in image side works are used to classify and analyze the scanned images. In recent years, Histopathology is used in medical sciences abundantly by which better results are getting produced. Pathology is a science which mainly works with effects and causes of various tissues. The images of those tissues are called whole-slide images. They are generally very big in size and occupy more amount of memory.A CNN model was developed to overcome this problem case where a better accuracy is produced with respect to the other models which also worked on the same objective.
Keywords
Breast Cancer, Histo-pathology images,Microscopy Whole-slided images.
Citations
IRE Journals:
Mada Dileep Kumar , Devana Mahesh Babu
"Convolution Neural Network using Deep Learning for Breast Cancer Analysis" Iconic Research And Engineering Journals Volume 3 Issue 12 2020 Page 114-119
IEEE:
Mada Dileep Kumar , Devana Mahesh Babu
"Convolution Neural Network using Deep Learning for Breast Cancer Analysis" Iconic Research And Engineering Journals, 3(12)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12