A scientist from Hidalgo presents a study in France that allows identifying breast cancer through AI; accuracy is 90%

A Mexican scientist has presented groundbreaking research in France that uses artificial intelligence to detect breast cancer with up to 90% accuracy, according to reports from El Universal. The study was unveiled during the SPIE Photonics Europe 2026 International Congress in Strasbourg, France. The researcher, Raúl Castro Ortega, works at the Polytechnic University of Tulancingo in Hidalgo state. Castro Ortega's work involves a system that employs convolutional neural networks with attention mechanisms to analyze mammographic thermograms. Unlike traditional methods, this model autonomously learns patterns associated with healthy and diseased tissue without requiring specialists to predefine image characteristics. This approach reduces processing time and improves result consistency. The study, titled "Analysis of Mammographic Thermography Using Convolutional Neural Networks with Attention Mechanisms," utilizes an algorithm based on the heat diffusion equation to focus analysis on key regions within thermal images. By doing so, the system classifies tissues with high sensitivity and specificity, achieving a 90% accuracy rate in detecting breast cancer. Castro Ortega explained that the AI-driven method allows for automated image analysis, which could significantly enhance early detection efforts. Traditional diagnostic techniques often rely on radiologists interpreting mammograms, a process that can be time-consuming and subject to human error. His system aims to streamline this process while maintaining a high level of reliability. The research highlights the potential of artificial intelligence in medical diagnostics, particularly in areas where access to specialized care is limited. Early detection of breast cancer is crucial for improving patient outcomes, and Castro Ortega’s findings suggest that AI could play a vital role in making such screenings more efficient and accessible. The technology relies on thermal imaging, which captures temperature variations in the body. These variations can indicate abnormal cellular activity, a hallmark of cancerous growths. By training the AI model on a dataset of thermal images, the system learns to distinguish between benign and malignant tissue with increasing precision. This innovation represents a step forward in the integration of AI into healthcare systems. It underscores the growing importance of machine learning in diagnosing diseases and managing health data. As AI continues to evolve, its applications in medicine are likely to expand, offering new tools for doctors and researchers alike. The presentation in France marks a significant moment for Mexican scientists working in the field of biomedical engineering. It demonstrates how local expertise can contribute to global scientific advancements. The success of this project may encourage further investment in AI research within Mexico, potentially leading to more collaborative international projects.