AIMC Topic: Diagnosis, Computer-Assisted

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A Fast SVM-Based Tongue's Colour Classification Aided by -Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

Journal of healthcare engineering
In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable ...

Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Computer-aided diagnosis (CAD) plays a vital role in the routine clinical activity for the detection of lung disorders using computed tomography (CT) images. It serves as a source of second opinion that radiologists may con...

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN.

BioMed research international
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new c...

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. ...

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

BMC medical informatics and decision making
BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (...

Use of a machine learning framework to predict substance use disorder treatment success.

PloS one
There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitat...

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.

Scientific reports
Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligen...

Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning ...

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PloS one
This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. I...

Feasibility of spirography features for objective assessment of motor function in Parkinson's disease.

Artificial intelligence in medicine
OBJECTIVE: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very impor...