QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals

G Tasci, MV Gun, T Keles, B Tasci, PD Barua… - Chaos, Solitons & …, 2023 - Elsevier
Background Severe psychiatric disorders, including depressive disorders, schizophrenia
spectrum disorders, and intellectual disability, have devastating impacts on vital life domains …

Automated ischemic acute infarction detection using pre-trained CNN models' deep features

B Tasci - Biomedical Signal Processing and Control, 2023 - Elsevier
Abstract Background Cerebrovascular Diseases (CVD) constitute more than 50% of
neurological diseases requiring hospital treatment. Stroke is a type of disease that causes …

[HTML][HTML] Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

JA Bojsen, MT Elhakim, O Graumann, D Gaist… - Insights into …, 2024 - Springer
Objectives This systematic review and meta-analysis aimed to assess the stroke detection
performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and …

[HTML][HTML] Attention deep feature extraction from brain MRIs in explainable mode: Dgxainet

B Taşcı - Diagnostics, 2023 - mdpi.com
Artificial intelligence models do not provide information about exactly how the predictions
are reached. This lack of transparency is a major drawback. Particularly in medical …

[HTML][HTML] Image Visualization and Classification Using Hydatid Cyst Images with an Explainable Hybrid Model

M Yildirim - Applied Sciences, 2023 - mdpi.com
Hydatid cysts are most commonly found in the liver, but they can also occur in other body
parts such as the lungs, kidneys, bones, and brain. The growth of these cysts occurs through …

A color-based deep-learning approach for tissue slide lung cancer classification

V Bishnoi, N Goel - Biomedical Signal Processing and Control, 2023 - Elsevier
Abstract Non-Small Cell Lung Cancer is the most common type of lung cancer, accounting
for more than 80% of all cases. The analysis of histopathological images is the appropriate …

Automated schizophrenia detection model using blood sample scattergram images and local binary pattern

B Tasci, G Tasci, H Ayyildiz, AP Kamath… - Multimedia Tools and …, 2024 - Springer
The main goal of this paper is to advance the field of automated Schizophrenia (SZ)
detection methods by presenting a pioneering feature engineering technique that achieves …

[HTML][HTML] Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis

T Ekmekyapar, B Taşcı - Diagnostics, 2023 - mdpi.com
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that
prominently affects young adults due to its debilitating nature. The pathogenesis of the …

[HTML][HTML] A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

F Zhou, S Hu, X Du, X Wan, J Wu - Future Internet, 2024 - mdpi.com
In the current field of disease risk prediction research, there are many methods of using
servers for centralized computing to train and infer prediction models. However, this …

[HTML][HTML] Monocyte/hdl cholesterol ratios as a new inflammatory marker in patients with schizophrenia

N Kılıç, G Tasci, S Yılmaz, P Öner… - Journal of Personalized …, 2023 - mdpi.com
Purpose: Monocyte/HDL cholesterol ratio (MHR) is a novel inflammatory marker that is used
as a prognostic factor for cardiovascular diseases and has been studied in many diseases …