Performance Comparison of Deep Learning-based Models on Breast Cancer Detection
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Keywords:
Breast Cancer, Deep Learning, Mammogram, Cancer Diagnosis, Early DetectionAbstract
Breast cancer is one of the most common types of cancer among women and early detection
can significantly reduce mortality rates. Traditional methods such as mammography can have difficulties
in detecting small and benign tumors and increase the risk of misdiagnosis. This study utilizes artificial
intelligence technologies for early detection of breast cancer. Because of this, it uses deep learning
models to analyze data from established imaging technologies such as mammography, MRI, and
ultrasound, and improved diagnostic accuracy is demonstrated. As a result of the experimental results
using Convolutional Neural Networks (CNN), EfficientNet, ResNet, and DenseNet models, the highest
success rate was achieved in the CNN model with an accuracy of 84.5%. As a result, this study aims to
support and accelerate the process of doctors' evaluation of patients by creating a model with high
accuracy, sensitivity, and specificity rates from deep learning models. This will allow diagnosis to
become more efficient and accessible while aiming to reduce medical errors and early diagnosis.
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