AIMC Topic: Deep Learning

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Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive...

Forecasting deep learning-based risk assessment of vector-borne diseases using hybrid methodology.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Dengue fever is rapidly becoming Malaysia's most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling ...

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis].

Vestnik oftalmologii
This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screenin...

DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.

Methods in molecular biology (Clifton, N.J.)
Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformatics prediction for tumor neoantigens has rapidly developed, focusing on the prediction of peptide-HLA...

Deep Learning-Based HLA Allele Imputation Applicable to GWAS.

Methods in molecular biology (Clifton, N.J.)
Human leukocyte antigen (HLA) imputation is an essential step following genome-wide association study, particularly when putative associations in HLA genes are identified, to fully understand the genetic basis of human traits. Different HLA imputatio...

Updated Models of Alzheimer's Disease with Deep Neural Networks.

Journal of Alzheimer's disease : JAD
BACKGROUND: In recent years, researchers have focused on developing precise models for the progression of Alzheimer's disease (AD) using deep neural networks. Forecasting the progression of AD through the analysis of time series data represents a pro...

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment.

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

Current medical imaging
INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) imag...

Enhancing Alzheimer's Disease Classification with Transfer Learning: Finetuning a Pre-trained Algorithm.

Current medical imaging
OBJECTIVE: The increasing longevity of the population has made Alzheimer's disease (AD) a significant public health concern. However, the challenge of accurately distinguishing different disease stages due to limited variability within the same stage...

Prostate Segmentation in MRI Images using Transfer Learning based Mask RCNN.

Current medical imaging
INTRODUCTION: The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it's the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, dee...