Purpose: The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy.
Materials And Method: This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.
Results: We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.
Conclusions: Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.
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http://dx.doi.org/10.1016/j.medengphy.2023.103971 | DOI Listing |
J Med Imaging (Bellingham)
January 2025
The University of Tokyo Hospital, Department of Radiology, Tokyo, Japan.
Purpose: The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance.
Approach: We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods.
Arab J Urol
August 2024
Urology Department, Hamad Medical Corporation, Doha, Qatar.
Background: Sociocultural aspects can impact sexual and reproductive health (SRH). Despite this, no study appraised the socio-cultural underpinnings impacting men's SRH in MENA (Middle East and North Africa). The current systematic review undertook this task.
View Article and Find Full Text PDFInt Urol Nephrol
January 2025
Department of Ultrasound, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, No. 2 Jiefang Road, Xiling District, Yichang, Hubei, China.
Objective: A prostate ultrasound (US) imaging omics model was established to assess its effectiveness in diagnosing prostate cancer (PCa), predicting Gleason score (GS), and determining the likelihood of distant metastasis.
Methods: US images of patients with prostate pathology confirmed by biopsy or surgery at our hospital were retrospectively analyzed. Regions of interest (ROI) segmentation, feature extraction, feature screening, and the construction and training of the radiomics model were performed.
Int J Cardiovasc Imaging
January 2025
Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA.
Our study aims to assess the robustness of myocardial radiomic texture features (RTF) to segmentation variability and variations across scanners with different field strengths, addressing concerns about reliability in clinical practices. We conducted a retrospective analysis on 45 pairs of CMR T1 maps from 15 healthy volunteers using 1.5 T and 3 T Siemens scanners.
View Article and Find Full Text PDFGenetics
January 2025
Dept. of Genetics, Stanford University, Stanford CA 94305-5120, USA.
The Candida Genome Database (CGD; www.candidagenome.org) is unique in being both a model organism database and a fungal pathogen database.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!