Year: 2024
Type: article
Abstract: Introduction: Breast cancer is one of the most common type of cancer with a high mortality rate. Mammography is widely used to identify breast cancer. Computer Aided Diagnosis systems are used for automatic detection of breast lesions. Methods: We propose and evaluate a deep learning model, called VGG16-C300, for breast mass malignancy classification. CBIS-DDSM dataset was used for training and evaluation. Image contrast enhancement methods like CLAHE and Mean Blur where previously applied to regions of interests. Results: The trained model achieved and area under the curve of 0.80, after 10 iterations of a 5-fold Cross-Validation. Conclusions: VGG16-C300 could be used as a component in a computer-aided diagnosis system for breast cancer detection
Institution: University of Havana
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405877800