Tag: computer-assisted diagnosis

Analyzing the Impact of Data Augmentation on Tumor Detection and Classification in Mammograms (2025)

Hybrid Artificial Intelligence Systems Authors Madalina Dicu, Enol García González, Camelia Chira, José R. Villar Abstract Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection crucial for improving survival rates. Deep learning-based approaches have shown remarkable potential in automating tumor detection from mammographic images; however, their effectiveness…

Preprocessing Techniques for Optimizing Mammograms Segmentation: a Cellular Automaton Approach (2025)

Procedia Computer Science Authors Iulia-Andreea Ion, Cristiana Moroz-Dubenco, A. Andreica Abstract Breast cancer is the most commonly diagnosed cancer among women worldwide, with early detection playing an essential role in improving survival rates. Detection of breast abnormalities at an early stage is best performed using mammography. This paper presents a new approach integrating advanced…

Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study (2025)

Diagnostics Authors T. Telecan, C. Caraiani, B. Boca, Roxana Sipos-Lascu, L. Dioşan, Z. Bálint, Raluca Maria Hendea, I. Andraș, Nicolae Crișan, M. Lupșor-Platon Abstract Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups,…

Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps (2024)

Clinical Otolaryngology Authors Anda Gâta, L. Raduly, Liviuța Budișan, Adél Bajcsi, Teodora-Maria Ursu, C. Chira, Laura-Silvia Dioşan, I. Berindan‐Neagoe, S. Albu Abstract Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence. Methods: We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. Preoperative…

Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks’ ensemble (2024)

Heliyon Authors Andra Carmen Marginean, Sorana Mureșanu, M. Hedeșiu, L. Dioşan Abstract Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in…

Towards an interpretable breast cancer detection and diagnosis system (2024)

Comput. Biol. Medicine Authors Cristiana Moroz-Dubenco, Adél Bajcsi, A. Andreica, C. Chira Abstract According to the World Health Organization, breast cancer becomes fatal only if it spreads throughout the body. Therefore, regular screening is essential. Whilst mammography is the most frequently used technique, its interpretation can be challenging and time-consuming. For this reason, computer-aided…

Significance of Training Images and Feature Extraction in Lesion Classification (2024)

International Conference on Agents and Artificial Intelligence Authors Adél Bajcsi, A. Andreica, C. Chira Abstract Proper treatment of breast cancer is essential to increase survival rates. Mammography is a widely used, non-invasive screening method for breast cancer. A challenging task in mammogram analysis is to distinguish between tumors. In the current study, we address…

Generalizing an Improved GrowCut Algorithm for Mammography Lesion Detection (2023)

Hybrid Artificial Intelligence Systems Authors Cristiana Moroz-Dubenco, L. Dioşan, A. Andreica Abstract In the past five years, 7.8 million women were diagnosed with breast cancer. Breast cancer is curable if it is discovered in early stages. Therefore, mammography screening is essential. But, since interpretation can prove difficult, various automated interpretation systems have been proposed…

Linear Discriminant Analysis Tumour Classification for Unsupervised Segmented Mammographies (2023)

International Conference on Knowledge-Based Intelligent Information & Engineering Systems Authors Cristiana Moroz-Dubenco, A. Andreica Abstract Between 2015 and 2020, 7.8 million women were diagnosed with breast cancer. If the cancer is discovered early, it can be completely cured. Computer-aided detection and diagnosis systems are a helpful tool. We propose such a system: after pre-processing…

Extended Mammogram Classification From Textural Features (2023)

Studia Universitatis Babeș-Bolyai Informatica Authors Adél Bajcsi, C. Chira, A. Andreica Abstract The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes…