Studia Universitatis Babeș-Bolyai Informatica Authors Attila Mester, A. Andreica Abstract We investigate the problem of breast cancer screening optimization, using various techniques applicable in domains where the data format is not defined in advance. The aim is to minimize the cost related to the screening of patients while maximizing the beneficial effect of the…
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…
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…
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…
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…
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…
International Conference on Virtual Storytelling Authors Cristiana Moroz-Dubenco, L. Dioşan, A. Andreica Abstract Breast cancer is the most frequent type of malignancy in women, with 2.3 million diagnostics only in 2020. However, as a consequence of early diagnosis and appropriate treatment, more and more women are being cured. Among screening methods, mammography is one…
International Conference on Knowledge-Based Intelligent Information & Engineering Systems Authors Iulia-Andreea Ion, Cristiana Moroz-Dubenco, A. Andreica Abstract Breast cancer is the most common type of cancer found in women. One of the most effiective methods for early identification of breast cancer is the mammogram. Numerous computer-aided systems for detecting breast cancer from mammograms have…
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…
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…
