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 largely depends on the choice of data augmentation strategies and model architecture. In this study, we…
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 preprocessing techniques based on Cellular Automaton. It was applied on the…
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, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have…
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 (demographic data, blood eosinophiles, endoscopy, Lund-Mackay, SNOT-22 and depression PHQ scores) and follow-up data was standardly collected. Outcome measures included SNOT-22, PHQ-9 and…
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 the detection of cavities in dental X-rays with 99.42% accuracy. Method: The first…
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 detection and diagnosis systems are increasingly being used for second opinion. However, in…
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 this problem using different feature extraction and classification methods. In the literature, numerous feature extraction…
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 so far. A crucial step of the interpretation process is segmentation: identifying…
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 the mammography, the region of interest is identified using an unsupervised manner. Textural features are…
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 (normal, benign and malignant). The performance of the system is evaluated for…
