AIMC Topic: Image Processing, Computer-Assisted

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Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can ...

Machine learning techniques for medical diagnosis of diabetes using iris images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for diseas...

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack...

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

Scientific reports
By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using th...

EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a deep learning based approach for facial action unit (AU) detection by enhancing and cropping regions of interest of face images. The approach is implemented by adding two novel nets (a.k.a. layers): the enhancing layers an...

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features.

Computers in biology and medicine
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of thes...

Multiscale High-Level Feature Fusion for Histopathological Image Classification.

Computational and mathematical methods in medicine
Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It c...

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.

Computational and mathematical methods in medicine
Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The inv...

Non-water-suppressed H FID-MRSI at 3T and 9.4T.

Magnetic resonance in medicine
PURPOSE: This study investigates metabolite concentrations using metabolite-cycled H free induction decay (FID) magnetic resonance spectroscopic imaging (MRSI) at ultra-high fields.

Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification.

Asian Pacific journal of cancer prevention : APJCP
Lung cancer is a frequently lethal disease often causing death of human beings at an early age because of uncontrolled cell growth in the lung tissues. The diagnostic methods available are less than effective for detection of cancer. Therefore an aut...