AIMC Topic: Algorithms

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Automatic evaluation of Nail Psoriasis Severity Index using deep learning algorithm.

The Journal of dermatology
Nail psoriasis is a chronic condition characterized by nail dystrophy affecting the nail matrix and bed. The severity of nail psoriasis is commonly assessed using the Nail Psoriasis Severity Index (NAPSI), which evaluates the characteristics and exte...

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologis...

Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

International journal of computer assisted radiology and surgery
OBJECTIVES: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 disti...

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Cell biochemistry and biophysics
Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain the critical genetic material needed for normal cell growth and functioning. The DNA structure patt...

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.

Tomography (Ann Arbor, Mich.)
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether t...

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.

BMC medical informatics and decision making
PURPOSE: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, b...

NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.

BMC genomics
BACKGROUNDS: The single-pass long reads generated by third-generation sequencing technology exhibit a higher error rate. However, the circular consensus sequencing (CCS) produces shorter reads. Thus, it is effective to manage the error rate of long r...

Estimation of the amount of pear pollen based on flowering stage detection using deep learning.

Scientific reports
Pear pollination is performed by artificial pollination because the pollination rate through insect pollination is not stable. Pollen must be collected to secure sufficient pollen for artificial pollination. However, recently, collecting sufficient a...

xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.

Scientific reports
Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinic...

MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.

Frontiers in cellular and infection microbiology
While the world struggles to recover from the devastation wrought by the widespread spread of COVID-19, monkeypox virus has emerged as a new global pandemic threat. In this paper, a high precision and lightweight classification network MpoxNet based ...