Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1486-1492· 0 citations· 18 references
Abstract
Exact and precise segmenting of nuclei in cancer diagnostics is a very important aspect of computer-assisted diagnosis and grading of carcinoma of the breast. Nonetheless, obscure medical findings pose a few challenges in training deep neural networks from the beginning. Through this research, we put forward a combination of deep learning structures on the basis of U-Net for automated breast nuclei segmentation in H&E stained histological findings and pictures, with a little more concentration on Triple Negative Breast Cancer (TNBC). The proposed structures were evaluated Baseline U-Net and U-Net types employing pretrained encoder backbones (VGG16, ResNet50, EfficientNetB0, and MobileNetV2). These different were assessed using Intersection over Union (IoU), Dice coefficient, precision based on picture elements in capturing photos, Precision–Recall evaluation, and ROC-AUC.
An end-to-end computer-aided diagnosis framework that performs lesion segmentation, region-of-interest (ROI) extraction, tumor stage estimation, benign/malignant classification, and visual explainability across two complementary imaging modalities is presented.
Ishita Rana, D. Shah, D. Variya· Journal of Intelligent Decis...· 0 citations
The proposed improved U-Net-based deep learning model for image segmentation provides an effective solution for automated, high-precision segmentation of breast ultrasound images, demonstrating considerable potential for clinical translation.
This study proposes DenseUNet169, a deep learning framework that integrates established DenseNet and UNet for systematic evaluation in breast cancer segmentation that improves gradient flow, facilitates efficient training, and enhances segmentation accuracy by focusing on visually salient regions within histopathologic...
Habib Rasi, Hossein Ebrahimnezhad, M. Sedaaghi· Journal of Medical Signals &...· 0 citations
Breast cancer remains a predominant cause of mortality among women, highlighting the importance of accurate early detection. Mammography can be hampered by breast density and radiation exposure, while ultrasound is safer and more accessible. However, ultrasound images have speckle noise, low contrast, and often blurred...
Gena Darma, Made Naradeon, Handika Pramesta et al.· International Conference on...· 0 citations
The findings demonstrate that MSA-Net greatly improves segmentation, particularly in tiny tumors, demonstrating its usefulness in real-world clinical contexts.
Beenish Hina, M. Maqsood, Asma Sattar et al.· Science in progress· 0 citations
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