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Differentiating age and sex in vertebral body CT scans – Texture analysis versus deep learning approach

Nurzynska Karolina, Piórkowski Adam, Strzelecki Michał, Kociołek Marcin, Banyś Robert Paweł, Obuchowicz Rafał

Biocybernetics and Biomedical Engineering

2024

Vol. 44, no. 1

20--30

The automated analysis of computed tomography (CT) scans of vertebrae, for the purpose of determining an individual’s age and sex constitutes a vital area of research. Accurate assessment of bone age in children facilitates the monitoring of their growth and development. Moreover, the determination of both age and sex has significant relevance in various legal contexts involving human remains. We have built a dataset comprising CT scans of vertebral bodies from 166 patients of diverse genders, acquired during routine cardiac examinations. These images were rescaled to 8-bit data, and textural features were computed using the qMaZda software. The results were analysed employing conventional machine learning techniques and deep convolutional networks. The regression model, developed for the automatic estimation of bone age, accurately determined patients’ ages, with a mean absolute error of 3.14 years and R2 = 0.79. In the context of classifying patient gender through textural analysis supported by machine learning, we achieved an accuracy of 69 %. However, the application of deep convolutional networks for this task yielded a slightly lower accuracy of 59 %.

Dekompozycja instrukcji rozgałęziających w procesie optymalizacji kodu

Aleksander Rafał, Piórkowski Adam

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

2023

nr 28

7--26

Tematem niniejszego artykułu była ocena możliwości ręcznej modyfikacji kodu źródłowego w celu optymalizacji czasu wykonania programu. W ramach projektu inżynierskiego zaproponowano autorskie techniki optymalizacyjne, wykorzystujące dekompozycję instrukcji rozgałęziających. Eksperymentalnie zbadano efektywność prezentowanej dekompozycji dla procesora z rodziny Intel x86, wykonującego operacje w sposób potokowy. W ramach niniejszej pracy przebadano kompilatory z rodziny Clang oraz GCC dla języka C i C++ oraz środowisko JVM.

The subject of this article was the manual modification of source code in order of reducing average execution time of certain repeated conditional move operations. This paper is concerned with proposed optimization by utilizing proper decomposition of branching instructions. This article described in detail conditional assignment operations as well as conditional operations and adequate CPU execution in regard of specific processor family architecture. Every discussed topic was supported with simplified comp diagrams. Experiments were conducted based of analysis of decompiled machine code and advanced analysis of CPU pipelining and branch predication mechanisms. Proposed optimized solutions were investigated based on processor from Intel x86 family. Examples of applications of mentioned code manipulations with adequate code snippets were presented with adequate performance analysis. As part of this work, the optimization capabilities of the Clang compiler for C and C++ were analysed and validated. Effectiveness of proposed optimization was experimentally tested also for compilers from collections GCC and for the most popular virtual machine environments such as JVM. Gathered cleared results were presented after carried out statistical analysis on column charts and in simplified tables.