AIMC Topic: Image Processing, Computer-Assisted

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Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Scientific reports
Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and patient care. Deep convolutional neural networks trained on MRI data can enhance diagnostic precisi...

Multi-scale fusion semantic enhancement network for medical image segmentation.

Scientific reports
The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatment. Although Transformer-based models are effective at capturing global context, they are often ineff...

Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.

Scientific reports
Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limited by subjective judgement as well as cumbersome manual measurement. The research aims to develop an...

A multi-modal graph-based framework for Alzheimer's disease detection.

Scientific reports
We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes [Formu...

A dual encoder network with multiscale feature fusion and multiple pooling channel spatial attention for skin scar image segmentation.

Scientific reports
Skin scar is a prevalent dermatological concern that impacts both aesthetic appearance and psychological well-being, making precise delineation of scar tissue essential for clinical treatment. To address the challenge of scar image segmentation, this...

CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning.

Scientific reports
Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accur...

Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.

Scientific reports
Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New ...

Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.

Scientific reports
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However...

Clustering cell nuclei on microgrooves for disease diagnosis using deep learning.

Scientific reports
Various diseases including laminopathies and certain types of cancer are associated with abnormal nuclear mechanical properties that influence cellular and nuclear deformations in complex environments. Recently, microgroove substrates designed to mim...

MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).

PloS one
This work aims to promote early and accurate diagnosis of Temporal Lobe Epilepsy (TLE) by developing state-of-the-art deep learning techniques, with the goal of minimizing the consequences of epilepsy on individuals and society. Current approaches fo...