Authors :
Dr. P. Bhaskar; Tahaseen Syed; Hima Varsha Daka; Nikhil Kumar Theegala; Manikanta Tulluri
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/5n6fp6nc
Scribd :
https://tinyurl.com/zzmxa4rr
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR707
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
To forecast breast cancer with the goal of
giving a thorough rundown of current developments in
the area. Given that breast cancer is among the world's
leading causes of mortality for women; improving patient
outcomes requires early detection. This study looks into
the ability to predict outcomes using a variety of machine
learning (ML) models, including random forests, logistic
regression, support vector machines, decision trees, k-
nearest neighbours, and deep learning neural networks,
in predicting the incidence of breast cancer from patient
data, including genetic markers, imaging results, and
demographics.
Aims to provide a comprehensive analysis of presetn
advancements, obstacles, and prospects in the field of
CNN-based techniques for breast cancer identification.
The review begins by outlining the urgent need for
reliable and accurate diagnostic methods for breast
cancer, highlighting the critical role that early
identification plays in enhancing patient outcomes.
Which delves into the intricate architecture of CNNs,
revealing its unique applicability to mammography image
analysis as well as their innate advantages in image
classification tasks. Important topics of discussion include
the various CNN architectures used for two- and three-
dimensional (2D) imaging methods used in breast cancer
diagnosis.
Keywords :
Breast, Cancer. Tumor, Resnet50 V2, VGG16, CNN.
To forecast breast cancer with the goal of
giving a thorough rundown of current developments in
the area. Given that breast cancer is among the world's
leading causes of mortality for women; improving patient
outcomes requires early detection. This study looks into
the ability to predict outcomes using a variety of machine
learning (ML) models, including random forests, logistic
regression, support vector machines, decision trees, k-
nearest neighbours, and deep learning neural networks,
in predicting the incidence of breast cancer from patient
data, including genetic markers, imaging results, and
demographics.
Aims to provide a comprehensive analysis of presetn
advancements, obstacles, and prospects in the field of
CNN-based techniques for breast cancer identification.
The review begins by outlining the urgent need for
reliable and accurate diagnostic methods for breast
cancer, highlighting the critical role that early
identification plays in enhancing patient outcomes.
Which delves into the intricate architecture of CNNs,
revealing its unique applicability to mammography image
analysis as well as their innate advantages in image
classification tasks. Important topics of discussion include
the various CNN architectures used for two- and three-
dimensional (2D) imaging methods used in breast cancer
diagnosis.
Keywords :
Breast, Cancer. Tumor, Resnet50 V2, VGG16, CNN.
