cancer detection


Cancer detection using Artificial Intelligence (AI) involves leveraging advanced machine learning algorithms and techniques to identify and diagnose cancer from various medical data sources. The goal is to enhance early detection, improve diagnostic accuracy, and potentially reduce the need for invasive procedures.

MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification

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Dec 23, 2024
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Effect sizes as a statistical feature-selector-based learning to detect breast cancer

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Nov 11, 2024
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SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation

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Dec 11, 2024
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Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers

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Nov 30, 2024
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An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types

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Jan 10, 2025
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LegoPET: Hierarchical Feature Guided Conditional Diffusion for PET Image Reconstruction

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Nov 25, 2024
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A new Time-decay Radiomics Integrated Network (TRINet) for short-term breast cancer risk prediction

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Dec 04, 2024
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MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy

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Dec 27, 2024
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Advancing Pancreatic Cancer Prediction with a Next Visit Token Prediction Head on top of Med-BERT

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Jan 03, 2025
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D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification

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Nov 17, 2024
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