AIMC Topic: Early Detection of Cancer

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Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier.

Asian Pacific journal of cancer prevention : APJCP
Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based cervical cancer detection and segmentation methodology is proposed. This proposed system consists of the fo...

Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results.

Lung cancer (Amsterdam, Netherlands)
OBJECTIVES: We evaluated whether machine learning may be helpful for the detection of lung cancer in FDG-PET imaging in the setting of ultralow dose PET scans.

A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Medical image analysis
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors o...

Machine learning for diagnostic ultrasound of triple-negative breast cancer.

Breast cancer research and treatment
PURPOSE: Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine lear...

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish...

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...