Breast Cancer Detection Using Deep Learning Technique Based On Ultrasound Image
원문 초록
Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. This work proposed a deep learning system that increased the accuracy of classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, the accuracy of proposed system was evaluated.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers
Rethinking Query-based Transformer for Continual Image Segmentation
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.