TY - JOUR A1 - Bilous, Nataliya A1 - Malko, Vladyslav A1 - Frohme, Marcus A1 - Nechyporenko, Alina T1 - Comparison of CNN-Based Architectures for Detection of Different Object Classes JF - AI N2 - (1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models. KW - EfficientDet KW - SSD KW - YOLO KW - deep learning KW - neural network KW - object detection Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19634 VL - 5 IS - 4 SP - 2300 EP - 2320 PB - MDPI ER - TY - JOUR A1 - Przymus, Piotr A1 - Rykaczewski, Krzysztof A1 - Martín-Segura, Adrián A1 - Truu, Jaak A1 - De Santa Pau, Enrique Carrillo A1 - Kolev, Mikhail A1 - Naskinova, Irina A1 - Gruca, Aleksandra A1 - Sampri, Alexia A1 - Frohme, Marcus A1 - Nechyporenko, Alina T1 - Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models JF - Frontiers in Microbiology N2 - Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward. KW - clasiffication KW - clustering KW - deep learning KW - natural language processing KW - microbiome Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19889 SN - 1664-302X VL - 15 PB - Frontiers ER - TY - JOUR A1 - Alekseeva, Victoriia A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Meniailov, Ievgen A1 - Chumachenko, Dmytro T1 - Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation JF - Electronics N2 - The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area. KW - CT data KW - deep learning KW - image segmentation KW - U-Net KW - decision support system KW - chronic odontogenic rhinosinusitis KW - machine learning KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17088 SN - 2079-9292 VL - 12 IS - 5 PB - MDPI ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Omelchenko, Vladyslav A1 - Alekseeva, Victoriia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Kovác, Michal ED - Izonin, Ivan ED - Chrétien, Stéphane T1 - Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning T2 - Proceedings of the 6th International Conference on Informatics & Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023 N2 - The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70%. KW - identification of personality KW - multislice computed tomography KW - deep learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0074-3609-1 UR - https://ceur-ws.org/Vol-3609/short2.pdf VL - 3609 SP - 167 EP - 173 PB - CEUR-WS.org ER - TY - JOUR A1 - Pfeil, Juliane A1 - Siptroth, Julienne A1 - Pospisil, Heike A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Moskalenko, Olga A1 - Yateem, Murad A1 - Nechyporenko, Alina T1 - Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition JF - Big Data and Cognitive Computing N2 - Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera. KW - human intestinal microbiome KW - next-generation sequencing KW - type 2 diabetes KW - deep learning KW - image classification Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17184 SN - 2504-2289 VL - 7 IS - 1 PB - MDPI ER -