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Globoke nevronske mreže v medicinski diagnostiki
Stoklas, Nac (Author), Kukar, Matjaž (Mentor) More about this mentor... This link opens in a new window

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Abstract
V zadnjih letih količina medicinskih podatkov ter preiskav strmo narašča. S tem se področju strojnega učenja odpira še več možnosti za raziskovalno delo, ki je v medicini prisotno že dlje časa. Tradicionalni pristopi k analizi podatkov so se izkazali za učinkovite, zadnje čase pa lahko opazimo porast v uporabi globokih nevronskih in natančneje konvolucijskih nevronskih mrež. Te so uporabne predvsem za slikovne podatke, začenjajo pa se uporabljati tudi na klasičnih atributnih podatkih. V našem primeru skušamo zgraditi napovedni model s pomočjo konvolucijskih nevronskih mrež, ki bi za vhod prejel navadne atributne podatke. Uporabimo podatkovni set, ki ima veliko različnih atributov (rezultati preiskav), veliko razredov (različne bolezni) in mnogo nedefiniranih vrednosti (na različnih bolnikih se opravijo različne preiskave, zato je v podatkih mnogo praznih mest). Na teh realnih podatkih preizkusimo različne zgradbe plitkih in globokih nevronskih mrež. Izkaže se, da so dobljeni rezultati primerljivi z najboljšimi doslej pridobljenimi na istih podatkih.

Language:Slovenian
Keywords:globoke nevronske mreže, medicinska diagnostika, strojno učenje
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2017
Views:642
Downloads:240
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Secondary language

Language:English
Title:Deep neural networks in medical diagnostics
Abstract:
In the last years the amount of medical data and research procedures is rapidly increasing. With it there are even more possibilities for machine learning research, which has been present in the medical fields for a long while. Traditional approaches to data analysis have proven to be useful. The same goes for deep neural networks and more specifically for convolutional neural networks, which have seen heavily increased use in the latest years. They are mostly used on image data, although lately we have seen examples of them being used on conventional attribute data. We try to build a model based on CNN which would receive regular attribute data as input. We use a data set, which has many attributes (research results), many classes (different diseases) and many undefined values (different patients require different procedures, which results in many undefined attributes). It is on this real life data that we test different structures of deep and shallow CNN's. We conclude that results obtained in this manner are comparable to the best results on this data so far.

Keywords:deep neural networks, medicinal diagnostics, machine learnin

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