Tag: breast cancer

A Systematic Analysis of Data Augmentation and Training Parameters for Lightweight CNN-Based Mammographic Lesion Classification (2026)

Hybrid Artificial Intelligence Systems Authors D. Trandafir, Mădălina Dicu, Camelia Chira Abstract Breast cancer remains a major cause of cancer-related mortality among women worldwide, making accurate lesion detection essential for improving survival rates and supporting clinical diagnosis. Although current pretrained models for predicting benign or malignant lesions achieve high accuracy, they often focus on…

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

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…

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

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…

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

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…

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

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…

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

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…

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

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…