Kevin Alejandro Hernández Gómez

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).Results: The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.Conclusions: The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other methods.

The education is a fundamental law that promotes the social and economic development of a nation. Also, it is how a country can achieve its sustainable development objectives. Because of this, various international organizations (Unicef-UNESCO- Banco Mundial -OECD) are promoting the coverage of education in underdeveloped countries. On the other hand, projects such as PISA that aim to carry out annual evaluations in more than 30 countries that serve as a reference of the educational level and encourages nations to improve the quality of education. Therefore, the objective of this work is to implement a methodology to predict the results of ICFES tests SABER 11 °. that can correct the educational problems that Colombia has presented in secondary education. For the development of this methodology, the ICFES repository database was used. Data were pre-processed using MATLAB software. Also, combined tests were carried out with the new Chi-Square metric developed by the researchers of the automation group of the Technological University of Pereira, obtaining results that were 20% higher compared with conventional classification techniques. In this project, the most influential characteristics were found in the students, who are responsible for the loss of the ICFES SABER 11° exam in Risaralda.