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Early Detection of Cancer

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Review of medical image recognition technologies to detect melanomas using neural networks.

BMC bioinformatics
BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of ...

Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

Annals of internal medicine
BACKGROUND: Lung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many ...

Mammographic image classification with deep fusion learning.

Scientific reports
To better address the recognition of abnormalities among mammographic images, in this study we apply the deep fusion learning approach based on Pre-trained models to discover the discriminative patterns between Normal and Tumor categories. We designe...

Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning.

PloS one
A new computer-aided detection scheme is proposed, the 3D U-Net convolutional neural network, based on multiscale features of transfer learning to automatically detect pulmonary nodules from the thoracic region containing background and noise. The te...

Using autoencoders as a weight initialization method on deep neural networks for disease detection.

BMC medical informatics and decision making
BACKGROUND: As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detecti...