Data mining techniques for AFM- based tumor classification

Abstract

The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterization of histological brain tumor samples. Data mining techniques will be applied for automatic identification of brain tumor tissues based on AFM images by means of classifying grade II and IV tumors. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the EulerPoincaré characteristic) as a feature descriptor to characterize global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.

Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

“…When using Euler-characteristics directly for classification, for each sample 256 features would need to be considered (i.e., the characteristic's values at 256 gray levels), which gives an unfavorable sample size to dimensionality ratio. However, even if [51] reported that it is possible to derive discriminative models directly out of Euler-characteristics data in such a case, some feature processing shall be applied in order to reduce the dataset's dimensionality. Since according to Figures 8 and 9 different Minkowski functionals (viz.…”

“…Euler-characteristics and contour length) can be well represented as curves, an obvious step is to reduce the measurements at 256 gray levels to some significant metrics regarding their curves. In this way, [51] presented the usage of 15 distinct geometrical features that have a sufficient descriptive nature for characterizing the Minkowski functionals, such as absolute value and position of extremum points, position of the zero-crossing, steepness measures, or areas under the curves. In this way, a dataset with 113 samples each of 15 features has been obtained.…”

“…It produces interpretable models that allow the assessment of the impact of each single feature in the Minkowski functional curves by this way optimizing the resulting classification accuracy [52, 53]. Analyzing the shape of both, Euler-characteristics and contour length, in all AFM images 15 characteristic features containing discriminative information were identified out of originally 256 ones [51]. Using GP, they were finally computed over all possible parameter configurations.…”

“…It enables better classification accuracy and works with a manageable number of features that can be extracted from both Minkowski functionals and finally leads to interpretable models. Further information on achievable classification models on this work can be obtained from [51]. …”

Gpfis: Um Sistema Fuzzy-Genético Genérico Baseado Em Programação Genética

“…Permite sintetizar programas de computador que solucionem problemas sem a necessidade de serem programados para tal. A partir da elaboração inicial de Koza [133], em pouco mais de 20 anos aárea se expandiu consideravelmente, abrangendo diferentes campos de aplicação, tais como Biotecnologia [105,198], Engenharia Elétrica [69,192] e Finanças [160,203], além de apresentar novas representações [31,152], entre as quais as mais conhecidas:…”