A comparison of computer aided learning and traditional didactic lectures for teaching clinical decision making skills to optometry undergraduates

This study was designed to compare computer aided learning (CAL), in the form of a Virtual Patient (VP), and traditional didactic lectures as methods of teaching clinical decision making (CDM) skills to second year Optometry undergraduates. Comparisons were based on performance in multiple-choice examinations testing CDM skills (actual mastery), student feedback relating to confidence in CDM skills (perceived mastery or self-efficacy) and student satisfaction. The influence of sex, learning style and academic ability was also investigated. This is the first time that these aspects of teaching pedagogy have been studied together. Current literature informed development of didactic lectures and an online VP. Both teaching methods were designed to ensure that the same clinical content was included. This content was aimed at training students to perform problem-orientated eye examinations. A cohort of 102 students was taught using the traditional didactic lectures in academic year 2010-11 and 93 students using the online VP in academic year 2011-12. An established Index of Learning Styles instrument was used to classify students according to their preference in four learning style dimensions. Both teaching methods were designed to cater for both poles of each learning style dimension. Most students had no strong learning preferences but those that did had a tendency towards the active-sensing-visual-sequential profile. Actual and perceived mastery were scored for five key learning objectives; question selection, critical symptom recognition, test selection, critical sign recognition and referral urgency selection. The influence of academic ability and teaching method differed for each learning objective; didactic lectures favouring some, the VP others. Learning style and sex had no influence, indicating that both teaching methods catered equally for males and females with all learning styles. Comparisons between perceived and actual mastery revealed poor self-assessment accuracy. Student satisfaction, rated on a five point Likert scale, was equally high for both teaching methods. Sex was the only influential variable, with males favouring one aspect of VP training. Overall, the findings suggest that CAL should be used to supplement traditional teaching rather than replace it in order to ensure that all students benefit equally. Future research may wish to focus on self-assessment accuracy as a means of improving academic performance

Desarrollo de un nuevo algoritmo de clasificación de tumores cerebrales mediante imágenes de resonancia magnética

Healthcare scientists determined how MRI images have indeed been highly beneficial in latest times in the investigation of the recognition and early identification of a brain disease. The main primary stages in analysing the brain MRI pictures are image pre-processing, segmentation, feature extraction, and classification. Among the crucial processes that can evaluate how well brain MRI scans can be classified and ultimately the condition it will indicate is feature extraction and segmentation. In this paper stage wise methods are described. In the first stage (pre-processing stage) different filters; like; median, wiener, anisotropic, non-local means as well as combined filters used. In the pre-processing part, combined wiener and anisotropic filter gives the best result. In the second stage (segmentation stage), multi-thresholding technique – cuckoo search algorithm used using different objective functions; like; ostu, kapur entropy, tsallis entropy and proposed. In the proposed method of the segmentation stage used cuckoo search algorithm using combined ostu and tsallis entopy as an objective function. In the third stage (feature extraction), discrete wavelet transform used and in the fourth stage (classification) support vector machine used. In each stage results are compared using different parameters and we got best output using proposed method.Los científicos sanitarios han determinado que las imágenes de resonancia magnética han sido muy beneficiosas en los últimos tiempos para la investigación del reconocimiento y la identificación precoz de enfermedades cerebrales. Las principales etapas primarias en el análisis de las imágenes de resonancia magnética del cerebro son el preprocesamiento de imágenes, la segmentación, la extracción de características y la clasificación. Entre los procesos cruciales que pueden evaluar lo bien que se pueden clasificar las imágenes de resonancia magnética del cerebro y, en última instancia, la enfermedad que indicarán, se encuentran la extracción de características y la segmentación. En este artículo se describen métodos por etapas. En la primera etapa (etapa de preprocesamiento) se utilizan diferentes filtros, como la mediana, wiener, anisotrópico, medios no locales, así como filtros combinados. En la parte de preprocesamiento, los filtros wiener y anisotrópico combinados dan el mejor resultado. En la segunda etapa (etapa de segmentación), la técnica de umbralización múltiple - algoritmo de búsqueda de cuco utilizado utilizando diferentes funciones objetivas; como; ostu, kapur entropía, tsallis entropía y propuso. En el método propuesto de la etapa de segmentación utilizado cuckoo algoritmo de búsqueda utilizando ostu combinado y tsallis entropía como función objetivo. En la tercera etapa (extracción de características), transformada wavelet discreta utilizada y en la cuarta etapa (clasificación) máquina de vectores soporte utilizado. En cada etapa se comparan los resultados utilizando diferentes parámetros y se obtienen los mejores resultados utilizando el método propuesto

Un nuevo algoritmo multiumbral para la segmentación de imágenes de resonancia magnética

Segmentation is a crucial stage in picture evaluation techniques. Brain magnetic resonance imaging has been accurately segmented, extensively studied because the use of these types of methods allows the detection and recognition of a wide range of disorders. Thresholding is a simple and effective method for segmenting images. But depending on how many thresholds are employed for segmentation, thresholding-based techniques have a tendency to cost more to compute. As a result, metaheuristic algorithms are a crucial tool for multilevel thresholding that aid in determining the best values. Using a novel cuckoo search (NCS) algorithm, we have suggested a method for segmenting MRI images that is more efficient. Three different objective functions (Otsu's method, Kapur entropy, and Tsallis entropy function) were utilised by comparing the output of the projected strategy with the Cuckoo Search (CS) algorithm.La segmentación es una etapa crucial en las técnicas de evaluación de imágenes. La segmentación precisa de imágenes de resonancia magnética cerebral se ha estudiado ampliamente porque el uso de este tipo de métodos permite detectar y reconocer una amplia gama de trastornos. El umbralaje es un método sencillo y eficaz para segmentar imágenes. Pero dependiendo de cuántos umbrales se empleen para la segmentación, las técnicas basadas en el umbral tienden a ser más costosas de calcular. En consecuencia, los algoritmos metaheurísticos son una herramienta crucial para el umbralado multinivel que ayudan a determinar los mejores valores. Utilizando un algoritmo de búsqueda cucú (NCS), hemos sugerido un método más eficiente para segmentar imágenes de resonancia magnética. Se utilizaron tres funciones objetivo diferentes (el método de Otsu, la entropía de Kapur y la función de entropía de Tsallis) comparando el resultado de la estrategia proyectada con el algoritmo de búsqueda del cuco (CS)