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.
Breast cancer remains one of the most prevalent malignancies affecting women worldwide, and early, accurate detection through medical imaging is central to improving survival outcomes. This paper presents an end-to-end computer-aided diagnosis (CAD) framework that performs lesion segmentation, region-of-interest (ROI)...
Ishita Rana, D. Shah, D. Variya· Journal of Intelligent Decis...· 0 citations
Breast cancer ultrasound images present challenges such as heterogeneous lesion morphologies, indistinct boundaries, and class imbalance. To overcome these difficulties, we propose an improved U-Net-based deep learning model for image segmentation. Specifically, we employ ResNet-34 as the encoder to form a ResNet34-UNe...
Background: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the urgent need for accurate and efficient diagnostic tools. Histopathological image analysis plays a critical role in diagnosis by enabling cellularlevel tissue examination. However, manual assessment is often...
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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