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Title: Machine learning regularity representation from biological patterns: a case study in a Drosophila neurodegenerative model
Author: Díez Hermano, Sergio
Tutor: Vegas Lozano, Esteban
Others: Universitat Oberta de Catalunya
Keywords: Drosophila melanogaster
machine learning
Issue Date: May-2017
Publisher: Universitat Oberta de Catalunya
Abstract: This work presents a fully automated classification pipeline of bright-field images based on HOG descriptors and machine learning techniques. An initial ROI extraction is performed applying TopHat morphological kernel and Euclidean distance to centroid thesholding. Image classification algorithms are trained on these ROIs (SVM, Decision Trees, Random Forest, CNN) and their performance is evaluated on independent, unseen datasets. HOG + gaussian kernel SVM (0.97 accuracy and 0.98 AUC) and fine-tune pre-trained CNN (0.98 accuracy and 0.99 AUC) yielded the best results overall.
Language: English
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

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