Towards Breast Cancer Diagnosis Using Multiple Mammography Views
Year: 2025
Type: article
Abstract: This study introduces a novel computer aided diagnosis system to diagnose breast cancer using two mammography views as input i.e. MLO and CC. The pipeline consists of a convolutional autoencoder that is trained to extract features from different mammograms’ views, and one-dimensional convolutional neural nework to classify the input embeddings into two classes i.e. benign or malignant. We compare the one-dimensional convolutional neural network classification results with a support vector machine trained on the same latent embeddings. We conclude that the combination of autoencoders and one-dimensional convolutional neural networks yield the best classification accuracy on the test set of the INbreast dataset.
Author: Louai Zaiter
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409786250