Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

Behavior patterns in hormonal treatments using fuzzy logic models

Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of one hundred and twenty-three (123) patients and five hundred and fifty-nine (559) treatments applied in relation to such patients provided by an assisted reproduction clinic, has been used to obtain the fuzzy models. As conclusion, this work corroborates some known clinic experiences, provides some new ones and proposes a set of questions to be solved in future experiments.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956- C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Spatial and topology feature extraction on batik pattern recognition: a review

Abstract. Batik is an Indonesian cultural heritage that has been recognized by UNESCO as an international cultural heritage on October 2, 2009. Patterns of batik produce geometric shapes unique, the number and name of the batik patterns make it difficult to recognize each motif. The objective classification of batik is split image into classes according to the pattern motif motive so easy to recognize in accordance with its feature. Batik can be classified based on the shape of the motive, namely geometric motifs, geometric motifs and motifs non specific. Spatial information is an important aspect of image processing such as computer vision and recognition structure / pattern in the context of modelling and resolution of the uncertainty caused by the ambiguity in the low-level features. Shortcomings inherent in combining two colours and spatial features are not adaptive pattern recognition process of the region across multiple images and histogram matching is not appropriate to capture the colours on the image content. This study discussed a model of spatial features and feature combinations topology with the aim to improve the validation batik image pattern recognition so that the level of the pattern recognition motif batik image could be better. Some of the features that have been used include colour features and spatial features. In addition, this paper discusses the possibility of combining the features in pattern recognition. This paper proposes a combination of features that will be able to improve the validation of image pattern recognition of batik

The Power of Location: Predictive Modeling and GIS

In the past two decades, nautical archaeology has turned its attention to identifying and locating the ships used during the Atlantic Slave Trade. While the archival evidence exists, only a small number of these ships has been found, and even less have been excavated. Spatial analysis tools like GIS can be a powerful tool to help further this research. This thesis is an exploration of how predictive modeling and GIS could make the identification of slave wrecks plausible, and an overview of the ethical issues that surround the use of GIS within the context of the African Diaspora. With more representative sampling of ships, archaeologists can continue analyzing the slave trade not only from the archival documents of the owners, but also from the artifacts of those on board. Locating and identifying wrecks that are suitable for excavation will add invaluable data to the understanding of this journey; yet, numerous ethical issues must be taken into consideration. As this data deals with a crucial element of the African Diaspora, the larger anthropological community must involve the present descendants of these captives. If GIS is used in a larger theoretical context, it should also actively engage with present-day community stakeholders