4 research outputs found

    Machine learning methods for binary and multiclass classification of melanoma thickness From dermoscopic images

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    Thickness of the melanoma is the most important factor associated with survival in patients with melanoma. It is most commonly reported as a measurement of depth given in millimeters (mm) and computed by means of pathological examination after a biopsy of the suspected lesion. In order to avoid the use of an invasive method in the estimation of the thickness of melanoma before surgery, we propose a computational image analysis system from dermoscopic images. The proposed feature extraction is based on the clinical findings that correlate certain characteristics present in dermoscopic images and tumor depth. Two supervised classification schemes are proposed: a binary classification in which melanomas are classified into thin or thick, and a three-class scheme (thin, intermediate, and thick). The performance of several nominal classification methods, including a recent interpretable method combining logistic regression with artificial neural networks (Logistic regression using Initial variables and Product Units, LIPU), is compared. For the three-class problem, a set of ordinal classification methods (considering ordering relation between the three classes) is included. For the binary case, LIPU outperforms all the other methods with an accuracy of 77.6%, while, for the second scheme, although LIPU reports the highest overall accuracy, the ordinal classification methods achieve a better balance between the performances of all classes

    Metal debris release from metal-on-metal hip arthroplasty : mechanism, quantification and clinical effects

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    PhD ThesisMetal on metal (MoM) hip replacements consist of a cobalt-chromium-molybdenum alloy femoral head which articulates against an acetabular cup manufactured from similar material. MoM hip replacements were introduced in the 1980s. It was thought that the overall reduction in volumetric wear as well as the avoidance of polyethylene would lead to greater longevity of these prostheses. There had been isolated reports of adverse tissue reactions with previous generations of MoM devices but it was thought that improved manufacturing technology would eliminate these problems. In the 1990s, the Birmingham Hip Resurfacing (BHR) was developed. The positive mid-term results of this device led to a rapid increase in the use of the BHR throughout the world. For obvious reasons, the enhanced stability large diameter bearings provided proved extremely attractive to surgeons and patients. Manufacturers therefore began to develop total hip replacement systems for patients unsuitable for the resurfacing procedure. These systems used bearings of size 36mm and greater, in Contrast to the existing 28mm Metasul device. From 2005 onwards there began to emerge increasing numbers of reports of local complications in the tissues adjacent to MoM prostheses. These reactions included sterile masses, tissue destruction and osteolysis. The incidence of these tissue reactions was unknown, as were the risk factors for their development. This piece of work sought to quantity the volumetric and linear wear rates of failed MoM hips and to investigate the relationship these wear rates and a number of clinical parameters. These parameters included blood, serum and hip fluid chromium and cobalt concentrations, and the macro and microscopic appearance of periprosthetic tissue at revision surgery. In this way it was hoped that component design, host and surgical factors leading to adverse tissue reactions could be identified and potentially eliminated

    A logistic radial basis function regression method for discrimination of cover crops in olive orchards

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    Olive (Olea europaea L.) is the main perennial Spanish crop. Soil management in olive orchards is mainly based on intensive and tillage operations, which have a great relevancy in terms of negative environmental impacts. Due to this reason, the European Union (EU) only subsidizes cropping systems which require the implementation of conservation agro-environmental techniques such as cover crops between the rows. Remotely sensed data could offer the possibility of a precise follow-up of presence of cover crops to control these agrarian policy actions, but firstly, it is crucial to explore the potential for classifying variations in spectral signatures of olive trees, bare soil and cover crops using field spectroscopy. In this paper, we used hyperspectral signatures of bare soil, olive trees, and sown and dead cover crops taken in spring and summer in two locations to evaluate the potential of two methods (MultiLogistic regression with Initial and Radial Basis Function covariates, MLIRBF; and SimpleLogistic regression with Initial and Radial Basis Function covariates, SLIRBF) for classifying them in the 400-900 nm spectrum. These methods are based on a MultiLogistic regression model formed by a combination of linear and radial basis function neural network models. The estimation of the coefficients of the model is carried out basically in two phases. First, the number of radial basis functions and the radii and centres' vector are determined by means of an evolutionary neural network algorithm. A maximum likelihood optimization method determines the rest of the coefficients of a MultiLogistic regression with a set of covariates that include the initial variables and the radial basis functions previously estimated. Finally, we apply forward stepwise techniques of structural simplification. We compare the performance of these methods with robust classification methods: Logistic Regression without covariate selection, MLogistic; Logistic Regression with covariate selection, SLogistic; Logistic Model Trees algorithm (LMT); the C4.5 induction tree; Naïve Bayesian tree algorithm (NBTree); and boosted C4.5 trees using AdaBoost.M1 with 10 and 100 boosting iterations. MLIRBF and SLIRBF models were the best discriminant functions in classifying sown or dead cover crops from olive trees and bare soil in both locations and seasons by using a seven-dimensional vector with green (575 nm), red (600, 625, 650 and 675 nm), and near-infrared (700 and 725 nm) wavelengths as input variables. These models showed a correct classification rate between 95.56% and 100% in both locations and seasons. These results suggest that mapping covers crops in olive trees could be feasible by the analysis of high resolution airborne imagery acquired in spring or summer for monitoring the presence or absence of cover crops by the EU or local administrations in order to make the decision on conceding or not the subsidy. © 2010 Elsevier Ltd. All rights reserved.This work has been partially financed by the TIN 2008-06681-C06-03 project (Spanish Inter-Ministerial Commission of Science and Technology), FEDER funds, and the P08-TIC-3745 project (Junta de Andalucia, Spain).Peer Reviewe

    Multilogistic Regression using Initial and Radial Basis Function covariates

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    This paper proposes a hybrid multilogistic model, named MultiLogistic Regression using Initial and Radial Basis Function covariates (MLRIRBF). The process for obtaining the coefficients is carried out in several steps. First, an Evolutionary Programming (EP) algorithm is applied, aimed to produce a RBF Neural Network (RBFNN) with a reduced number of RBF transformations and the simplest structure possible. Then, the input space is transformed by adding the nonlinear transformations of the input variables given by the RBFs of the best individual in the last generation. Finally, a maximum likelihood optimization method determines the coefficients associated with a multilogistic regression model built on this transformed input space. In this final step, two different multilogistic regression algorithms are applied, one that considers all initial and RBF covariates (MLRIRBF) and another one that incrementally constructs the model and applies cross-validation, resulting in an automatic covariate selection (MLRIRBF*). The methodology proposed is tested using six benchmark classification problems from well-known machine learning problems. The results are compared with the corresponding multilogistic regression methodologies applied over the initial input space, to the RBFNNs obtained by the EP algorithm (RBFEP) and to other competitive machine learning techniques. The MLRIRBF* models are found to be better than the corresponding multilogistic regression methodologies and the RBFEP method for almost all datasets, and obtain the highest mean accuracy rank when compared to the rest of methods in all datasets
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