Browsing by Author "Paula Filho, Pedro Luiz de"
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- Colorectal Polyp Segmentation: Impact of Combining Different Datasets on Deep Learning Model PerformancePublication . Araujo, Sandro Luis de; Scheeren, Michel Hanzen; Aguiar, Rubens Miguel Gomes; Mendes, Eduardo; Franco, Ricardo Augusto Pereira; Paula Filho, Pedro Luiz deColorectal cancer is a major health concern, ranking as one of the most common and deadly forms of cancer. It typically begins as polyps, which are abnormal growths in the intestinal mucosa. Identifying and removing these polyps through colonoscopy is a crucial preventative measure. However, even experienced professionals can overlook some polyps during examinations. In this context, segmentation algorithms can assist medical professionals by identifying areas in an image that correspond to a polyp. These algorithms, which rely on deep learning, require extensive image datasets to effectively learn how to identify and segment polyps. This study aimed to identify public colonoscopy image datasets that contain polyps and to examine how combining these datasets might affect the performance of a deep learning-based segmentation algorithm. After selecting the datasets and defining their combinations, we trained a segmentation algorithm on each combination. The evaluation of the trained models showed that merging datasets can enhance model generalization, with increases of up to 0.242 in the dice coefficient and 0.256 in the Intersection over Union (IoU). These improvements could lead to higher diagnostic accuracy in clinical settings, enhancing efforts to prevent colorectal cancer.
- Image processing of petri dishes for counting microorganismsPublication . Barbosa, Marcela; Santos, Everton; Teixeira, João Paulo; Mendonca, Saraspathy; Candido Junior, Arnaldo; Paula Filho, Pedro Luiz deFor a food to be considered functional it is necessary to prove that it has microorganisms in its composition. In order to determine the presence of microorganisms in a food, laboratory analyzes can be carried out using of Petri dishes, which must pass through an incubation period, to then manually count the number of bacterial colonies that are present in each board. Therefore, the objective of this work was to develop a software to perform the automated counting of colonies contained in Petri plates and to validate the efficiency of the tool through comparisons with the results of a manual count. For this, an algorithm was developed based on digital image processing techniques capable of identifying the Petri dish within the image and counting the number of colonies present on each plate. As a result, a global correlation of 0.948 was observed in relation to the manual count, and in an individual analysis, a correlation of 0.8134 was obtained. Thus, it can be concluded that counts of microorganisms can be performed automatically and reliably with the developed software.
- Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their MicrobiomePublication . Barque, Barry Malick; Rodrigues, Pedro João; Paula Filho, Pedro Luiz de; Peixoto, Raquel Silva; Leite, Deborah Catharine de AssisOne of the most diverse and productive marine ecosystems in the world are the corals, providing not only tourism but also an important economic contribution to the countries that have them on their coasts. Thanks to genome sequencing techniques, it is possible to identify the microorganisms that form the coral microbiome. The generation of large amounts of data, thanks to the low cost of sequencing since 2005, provides an opening for the use of artificial neural networks for the advancement of sciences such as biology and medicine. This work aims to predict the healthy microbiome present in samples of Mussismilia hispida coral, using machine learning algorithms, in which the algorithms SVM, Decision Tree, and Random Forest achieved a rate of 61%, 74%, and 72%, respectively. Additionally, it aims to identify possible microorganisms related to the disease in question in corals.