Authors :
Asiya Anjum; Mohd Abdul Hameed
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Uao6rJ
DOI :
https://doi.org/10.5281/zenodo.7278643
Abstract :
- Due to the highest mortality rate in the globe,
cancer still poses a severe threat to individuals today. It is
because there is insufficient early cancer detection. One of
the most deadly diseases for women worldwide and the most
frequently detected non-skin cancer in women is breast
cancer carcinoma. The probability of a successful treatment
is significantly increased by diagnostic testing of this lifethreatening disease. The traditional method of diagnosing
cancer highly depends on the technician’s or doctor’s ability
in spotting anomalies in the human body. Oncethe breast’s
cell tissue starts to divide irregularly and aggressively,
breast cancer develops. Bosom malignant tumor can be
classified into five stages (0–IV), each of which reflects the
severity of the disease within the patient’s body. Mammary
gland illness is complicated and has many varieties of
clinical effects, which makes it difficult to predict and treat.
Additionally, the ability to more accurately predict the
onset of a malignant infection will benefit breast cancer
patients in planning their future course of treatment under
their doctor’s directions. It is particularly challenging to
predict breast cancer because of its great heterogeneity and
complicated features. This projectexamines the accuracy of
multiple CNN models and focuses on the prediction of
Breast disease using mammography images.Therefore in
the study, we explore various CNN models to identify
malignancy in mammography scans, including ResNet- 50,
VGG-16, Alex Net, and Google Net. Additionally, we are
going to build a special Xception model with 70% accuracy
to diagnose breast cancer and compare each model.
Keywords :
Alex Net, Breast Cancer, Carcinoma, CNNConvolutional Neural Network, Deep Learning, Diagnose, Google Net, Mammogram, Malignant infection, Machine learning, ResNet-50, VGG-16.
- Due to the highest mortality rate in the globe,
cancer still poses a severe threat to individuals today. It is
because there is insufficient early cancer detection. One of
the most deadly diseases for women worldwide and the most
frequently detected non-skin cancer in women is breast
cancer carcinoma. The probability of a successful treatment
is significantly increased by diagnostic testing of this lifethreatening disease. The traditional method of diagnosing
cancer highly depends on the technician’s or doctor’s ability
in spotting anomalies in the human body. Oncethe breast’s
cell tissue starts to divide irregularly and aggressively,
breast cancer develops. Bosom malignant tumor can be
classified into five stages (0–IV), each of which reflects the
severity of the disease within the patient’s body. Mammary
gland illness is complicated and has many varieties of
clinical effects, which makes it difficult to predict and treat.
Additionally, the ability to more accurately predict the
onset of a malignant infection will benefit breast cancer
patients in planning their future course of treatment under
their doctor’s directions. It is particularly challenging to
predict breast cancer because of its great heterogeneity and
complicated features. This projectexamines the accuracy of
multiple CNN models and focuses on the prediction of
Breast disease using mammography images.Therefore in
the study, we explore various CNN models to identify
malignancy in mammography scans, including ResNet- 50,
VGG-16, Alex Net, and Google Net. Additionally, we are
going to build a special Xception model with 70% accuracy
to diagnose breast cancer and compare each model.
Keywords :
Alex Net, Breast Cancer, Carcinoma, CNNConvolutional Neural Network, Deep Learning, Diagnose, Google Net, Mammogram, Malignant infection, Machine learning, ResNet-50, VGG-16.
