TH-E-L100J-01: Ultrasound Reflectivity Imaging with a Split-Step Fourier Propagator for Cancer Detection and Diagnosis in Heterogeneous Breasts

2007 ◽  
Vol 34 (6Part24) ◽  
pp. 2645-2645
Author(s):  
L Huang ◽  
K Hanson ◽  
Y Quan ◽  
R Pratt ◽  
C Li ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Detection And Diagnosis ◽  
Reflectivity Imaging

scholarly journals Nanotechnology: a promising method for oral cancer detection and diagnosis

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Xiao-Jie Chen ◽  
Xue-Qiong Zhang ◽  
Qi Liu ◽  
Jing Zhang ◽  
Gang Zhou
Keyword(s):  
Oral Cancer ◽  
Cancer Detection ◽  
Promising Method ◽  
Detection And Diagnosis

scholarly journals Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

2021 ◽  
Vol 2 (01) ◽  
pp. 41-51
Author(s):  
Jwan Saeed ◽  
Subhi Zeebaree
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Deep Convolutional Neural Networks ◽  
Color Structure ◽  
Malignant Skin ◽  
Dermatological Disorders ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

Cancer Detection and Diagnosis

2015 ◽  
pp. 30-50
Author(s):  
Daniel Seddon ◽  
Paul Mackenzie
Keyword(s):  
Cancer Detection ◽  
Detection And Diagnosis

scholarly journals Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Current Level ◽  
Breast Cancer Detection ◽  
Cad Systems ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

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