Cross-Domain Transfer Learning Architecture for Microcalcification Cluster Detection Using the MEXBreast Multiresolution Mammography Dataset

Ricardo Salvador Luna Lozoya (), Humberto de Jesús Ochoa Domínguez (), Juan Humberto Sossa Azuela, Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas and Karina Núñez Barragán ()
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Ricardo Salvador Luna Lozoya: Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Humberto de Jesús Ochoa Domínguez: Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Juan Humberto Sossa Azuela: Laboratorio de Robótica y Mecatrónica, Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, Mexico
Vianey Guadalupe Cruz Sánchez: Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Osslan Osiris Vergara Villegas: Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Karina Núñez Barragán: Clínica de Radiodiagnóstico e Imagen de Chihuahua, Chihuahua 31203, Mexico

Mathematics, 2025, vol. 13, issue 15, 1-24

Abstract: Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 μ m. The architecture processes individual 1 cm 2 patches extracted from the mammograms as input samples and includes a MobileNetV2 backbone, followed by a flattening layer, a dense layer, and a sigmoid activation function. This architecture was trained to detect MCCs using patches extracted from the INbreast database, which has a resolution of 70 μ m, and achieved an accuracy of 99.84%. We applied transfer learning (TL) and trained on 50, 70, and 100 μ m resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. For comparison purposes, models trained from scratch, without leveraging knowledge from the pretrained model, achieved 96.07%, 99.20%, and 83.59% accuracy for 50, 70, and 100 μ m, respectively. Results demonstrate that TL improves MCC detection across resolutions by reusing pretrained knowledge.

Keywords: breast cancer; microcalcification cluster detection; deep learning; convolutional neural network; transfer learning; MobileNetV2 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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