Hybrid EfficientNet B4 and SVM framework for rapid and accurate bone cancer diagnosis from X-rays

Nature·Mar 3

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Researchers are exploring various deep learning frameworks for the diagnosis of bone cancer from medical images. Different approaches, including hybrid models and transformer-based architectures, are being developed and compared for their accuracy and efficiency in detecting bone cancer. The specific details and effectiveness of these frameworks are being investigated and reported in various studies.

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ROI-guided relational YOLO–SegNet transformer for lightweight bone tumor segmentation and classification from X-ray images

Nature·Mar 23

Enhanced classification prostate cancer based on generative adversarial networks and integrated deep learning with vision transformer models

Nature·Dec 24

Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset

Nature·Nov 7

A hybrid deep learning framework using convolutional and transformer models for robust plant disease classification

Nature·Feb 18

Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements

Nature·Aug 1

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images

Nature·Jan 25

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