Model for the early detection of breast cancer using medical images (MDTCM)

This project addresses the critical need to improve the accuracy of early breast cancer diagnosis, a disease with high prevalence and serious public health implications, particularly in regions like Ecuador and Manabí. The main objective is to create an advanced diagnostic model that integrates the power of Soft Computing and Deep Learning. This approach seeks to overcome the limitations of traditional statistical methods and enhance existing Clinical Decision Support Systems (CDSS). The methodology employed is rigorous, including analysis-synthesis, induction-deduction, structural-systemic modeling, and practical experimentation. Techniques such as surveys and in-depth interviews with specialists will be used to validate requirements and results. The proposed model will be evaluated using the Osgood scale and inter-methodological triangulation to ensure the reliability and high certainty of its diagnoses, with the expected impact of enhancing the quality of medical-assistance processes in the prediction and classification of this disease.<br/><br/><b>Goal</b>: <br/>To develop an early breast cancer diagnosis model by combining Soft Computing techniques and deep learning, aiming to provide information with a higher degree of certainty to support clinical decision-making.<br/><br/><b>Research lines</b>: <br/>Computer systems and artificial intelligence