AI Medical Compendium Topic:
Early Detection of Cancer

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A review of computer aided detection in mammography.

Clinical imaging
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography r...

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer.

Sensors (Basel, Switzerland)
Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning techniqu...

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned proble...

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Artificial intelligence in medicine
Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially ...

The utilisation of convolutional neural networks in detecting pulmonary nodules: a review.

The British journal of radiology
Lung cancer is one of the leading causes of cancer-related fatality in the world. Patients display few or even no signs or symptoms in the early stages, resulting in up to 75% of patients diagnosed in the later stages of the disease. Consequently, th...

Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.

Digestive diseases and sciences
BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured o...

Detecting and classifying lesions in mammograms with Deep Learning.

Scientific reports
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimat...

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

Physics in medicine and biology
Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive pr...

Acral melanoma detection using a convolutional neural network for dermoscopy images.

PloS one
BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the h...