Early Detection and Classification of Breast Cancer Using Deep Learning Techniques
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ID: 282831
2025
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Abstract
Breast cancer is one of the deadliest cancers causing about massive number of
patients to die annually all over the world according to the WHO. It is a kind
of cancer that develops when the tissues of the breast grow rapidly and
unboundly. This fatality rate can be prevented if the cancer is detected before
it gets malignant. Using automation for early-age detection of breast cancer,
Artificial Intelligence and Machine Learning technologies can be implemented
for the best outcome. In this study, we are using the Breast Cancer Image
Classification dataset collected from the Kaggle depository, which comprises
9248 Breast Ultrasound Images and is classified into three categories: Benign,
Malignant, and Normal which refers to non-cancerous, cancerous, and normal
images.This research introduces three pretrained model featuring custom
classifiers that includes ResNet50, MobileNet, and VGG16, along with a custom
CNN model utilizing the ReLU activation function.The models ResNet50,
MobileNet, VGG16, and a custom CNN recorded accuracies of 98.41%, 97.91%,
98.19%, and 92.94% on the dataset, correspondingly, with ResNet50 achieving the
highest accuracy of 98.41%.This model, with its deep and powerful architecture,
is particularly successful in detecting aberrant cells as well as cancerous or
non-cancerous tumors. These accuracies show that the Machine Learning methods
are more compatible for the classification and early detection of breast
cancer.
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| Authors | Mst. Mumtahina Labonno; D. M. Asadujjaman; Md. Mahfujur Rahman; Abdullah Tamim; Mst. Jannatul Ferdous; Rafi Muttaki Mahi |
| Journal | arXiv |
| Year | 2025 |
| DOI |
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