Vocal Biomarkers for Parkinson's Disease Classification Using Audio Spectrogram Transformers.

Parkinson's disease (PD) is a neurodegenerative disorder affecting motor and non-motor functions, including speech. This study evaluates the effectiveness of the audio spectrogram transformer (AST) model in detecting PD through vocal biomarkers, hypothesizing that its self-attention mechanism would better capture PD related speech impairments compared to traditional deep learning approaches. Speech recordings from 150 participants (100 from PC-GITA: 50 PD, 50 healthy controls (HC); 50 from Italian Parkinson's voice and speech (ITA): 28 PD, 22 HC) were analyzed using the AST model and compared against established architectures including VGG16, VGG19, ResNet18, ResNet34, vision transformer, and swin transformer.

Accurate Acupoint Localization in 2D Hand Images: Evaluating HRNet and ResNet Architectures for Enhanced Detection Performance.

Introduction: This research assesses HRNet and ResNet architectures for their precision in localizing hand acupoints on 2D images, which is integral to automated acupuncture therapy.

Objectives: The primary objective was to advance the accuracy of acupoint detection in traditional Korean medicine through the application of these advanced deep-learning models, aiming to improve treatment efficacy.

Background: Acupoint localization in traditional Korean medicine is crucial for effective treatment, and the study aims to enhance this process using advanced deep-learning models.

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns.

Speech impairments often emerge as one of the primary indicators of Parkinson's disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data.

Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs.

Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&E) staining techniques. In this study, we investigate the impact of H&E stain normalization on the performance of DL models in cancer image classification.

Diagnosing Alzheimer's Disease Based on Multiclass MRI Scans using Transfer Learning Techniques.

Aims: To prevent Alzheimer's disease (AD) from progressing to dementia, early prediction and classification of AD are important and they play a crucial role in medical image analysis.

Background: In this study, we employed a transfer learning technique to classify magnetic resonance (MR) images using a pre-trained convolutional neural network (CNN).

Objective: To address the early diagnosis of AD, we employed a computer-assisted technique, specifically the deep learning (DL) model, to detect AD.

One-year Follow-up Study of Hippocampal Subfield Atrophy in Alzheimer's Disease and Normal Aging.

Background: In this study, we investigated the effect of hippocampal subfield atrophy on the development of Alzheimer's disease (AD) by analyzing baseline magnetic resonance images (MRI) and images collected over a one-year follow-up period. Previous studies have suggested that morphological changes to the hippocampus are involved in both normal ageing and the development of AD. The volume of the hippocampus is an authentic imaging biomarker for AD.

Deep Learning for Alzheimer's Disease Classification using Texture Features.

Background: We propose a classification method for Alzheimer's disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD.

Methods: We obtained magnetic resonance images (MRIs) of Alzheimer's patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. This dataset consists of image data for AD, mild cognitive impairment (MCI), and normal controls (NCs), classified according to the cognitive condition.

Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

Background: In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain.

Methods: The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software.

Alzheimer's Disease Classification Based on Multi-feature Fusion.

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer's Disease (AD).

Methods: In particular, we classified subjects with Alzheimer's disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis.

Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features.

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5.