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AN EFFICIENT FRAMEWORK USING LEARNING ALGORITHM FOR BREAST CANCER DIAGNOSTICS
Authors
P. Senthil Kumar,G. Buvaanyaa,Dr. M. Gobi
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Keywords
Accuracy, Gene expression analysis, Breast Cancer, Deep Learning, Classification, Prediction.
Abstract
Breast cancer affects more women than any other form of cancers. Breast cancer is diagnosed mostly by mammography. Medical data from CT scans, PET scans, and MRIs are among the most widely used types of information. The use of Deep learning and Machine Learning approaches has become essential for efficient and precise cancer prediction and detection since the work of analyzing this massive amount of data has gotten increasingly difficult. Clinically relevant information can be mined from medical photographs to better aid in illness diagnosis and early detection, which is the primary focus of medical image mining. Patients need careful symptom observation and a prediction automatic system that can identify the tumour as benign or malignant in order to receive effective treatment. While its primary function as a generic neural network based on convolution is to classify images with an image as input and a single label as output, in biomedical applications, it may also identify disease and locate its exact location. This issue can be solved by employing deep learning methods. A Deep learning with hybrid-based framework is suggested for tumour zone segmentation and prediction in mammography images. To assess the stage of breast cancer, the model uses a fully hybrid method that has been updated and broadened in design to operate with fewer training images and deliver more accurate tumour height and width segmentations.
IJCRT's Publication Details
Unique Identification Number - IJCRT2511315
Paper ID - 296435
Author type - Indian Author
Page Number(s) - c449-c458
Pubished in - Volume 13 | Issue 11 | November 2025
DOI (Digital Object Identifier) -
No Of Downloads - 67
Author Country - India, 641602, Tiruppur, Tiruppur, 641602, Science All
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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P. Senthil Kumar,G. Buvaanyaa,Dr. M. Gobi, "AN EFFICIENT FRAMEWORK USING LEARNING ALGORITHM FOR BREAST CANCER DIAGNOSTICS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 11, pp.c449-c458, November 2025, Available at :http://www.ijcrt.org/papers/IJCRT2511315.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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