Category: News

At the IT2025 IEEE conference in Žabljak, researchers from the University of Donja Gorica presented their latest study on the use of artificial intelligence (AI) for breast cancer diagnostics. The research explores the application of deep learning models, ResNet152 and DenseNet121, for analyzing mammographic images. In addition to clinical results, the study highlights the implications of using high-performance computing (HPC) infrastructure to optimize model training and evaluation. By transferring the experimental setup to HPC resources, the research opens pathways for faster development cycles, exploration of more complex architectures, and scalability for real-world implementation.

ABSTRACT – Artificial Intelligence is rapidly advancing the medical field by providing innovative disease diagnosis, treatment, and research approaches. This study explores the application of artificial intelligence in breast cancer diagnostics, focusing on using convolutional neural networks and deep learning to analyze mammographic images. ResNet152 and DenseNet121 models were used to classify malignant changes, achieving AUC scores exceeding 0.9, demonstrating their clinical utility. The research emphasizes how artificial intelligence can enhance screening efficiency, expedite diagnostic processes, and facilitate personalized treatment approaches. Ethical considerations, including patient safety and the transparency of artificial intelligence systems, were also analyzed. The findings underscore the potential of artificial intelligence to transform diagnostic procedures for breast cancer and highlight the importance of further research to integrate these technologies into clinical practice.

Researchers from the University of Donja Gorica presented a deep learning model for automated tuberculosis detection from chest X-rays at IEEE IT2025 conference. Using a convolutional neural network (CNN), the model classifies images as normal or tuberculosis-positive with an impressive 97.55% accuracy. This breakthrough has the potential to speed up diagnoses, reduce radiologist workload, and improve early detection rates, particularly in low-resource healthcare settings. By leveraging AI for fast and reliable medical imaging analysis, this research highlights the growing role of computer vision in modern healthcare and its ability to enhance efficiency and accuracy in disease detection.

ABSTRACT – This article presents a deep learning model that enables fast and accurate diagnosis of tuberculosis based on chest X-rays. The developed model uses convolutional neural network that enable the automatic classification of chest x-rays into one of two classes: Normal or Tuberculosis with a high degree of accuracy. The model achieved an accuracy of 97.55% on the test data set, indicating its potential to open new perspectives for medical professionals in establishing a tuberculosis diagnosis. This model can significantly speed up the diagnostic process, reducing the workload of medical workers and increasing their productivity in the fight against tuberculosis, one of the most common lung diseases.

At the IT2025 IEEE Conference in Žabljak, researchers from the University of Donja Gorica presented a study on voice cloning and text-to-speech (TTS) technology for cultural heritage preservation. Their research compared state-of-the-art AI models, including Realtime Voice Cloning (RVC), Tortoise AI, Bark, and Coqui AI, to evaluate how small, high-quality datasets can produce more accurate and natural-sounding speech than large, unstructured ones. The study highlights the potential of AI in preserving the Montenegrin language and oral traditions, enabling the creation of audiobooks, digital archives, and interactive experiences. This research paves the way for more accessible educational resources and enhanced cultural engagement using AI-driven speech synthesis.

ABSTRACT – This research presents a comparative analysis of modern voice cloning systems, focusing on their ability to generate high-quality speech from limited training data. The paper aims to demonstrate that carefully curated smaller datasets can produce superior results to larger, less structured datasets. The investigation of multiple state-of-the-art models, including Realtime Voice Cloning (RVC), Tortoise AI, Bark, and Coqui AI, establishes optimal data preparation protocols and identifies critical factors in training data quality, with particular emphasis on applications for the Montenegrin language and cultural preservation.

At the IT2025 IEEE Conference in Žabljak, researchers from the University of Donja Gorica presented their latest study on using Artificial Intelligence (AI) for breast cancer diagnostics. The research explores the application of deep learning models, ResNet152 and DenseNet121, to analyze mammographic images. Beyond the clinical results, the study emphasizes the implications of leveraging high-performance computing (HPC) infrastructure to optimize model training and evaluation. By porting the experimental setup to HPC resources, the research opens pathways for faster development cycles, the exploration of more complex architectures, and scalability for real-world implementation. 

ABSTRACT – Artificial Intelligence is rapidly advancing the medical field by providing innovative disease diagnosis, treatment, and research approaches. This study explores the application of artificial intelligence in breast cancer diagnostics, focusing on using convolutional neural networks and deep learning to analyze mammographic images. ResNet152 and DenseNet121 models were used to classify malignant changes, achieving AUC scores exceeding 0.9, demonstrating their clinical utility. The research emphasizes how artificial intelligence can enhance screening efficiency, expedite diagnostic processes, and facilitate personalized treatment approaches. Ethical considerations, including patient safety and the transparency of artificial intelligence systems, were also analyzed. The findings underscore the potential of artificial intelligence to transform diagnostic procedures for breast cancer and highlight the importance of further research to integrate these technologies into clinical practice.