Results 181 to 190 of about 9,112 (254)

Scanner‐agnostic artificial intelligence approach for fast bone scintigraphy

Journal of Applied Clinical Medical Physics, Volume 27, Issue 8, August 2026.
Abstract Purpose Current bone scintigraphy protocols often demand full‐count, 10–15 min scans to preserve image quality, and existing deep‐learning (DL) denoisers typically need to be retrained or retuned for each camera manufacturer. We introduce a scanner‐agnostic adaptive‐diffusion U‐Net designed to reconstruct diagnostic‐grade images from half‐time
Vinicius de Oliveira Menezes, Cleiton Cavalcante Queiroz, Antônio Augusto Silva Oliveira, Aurílio José dos Santos Filho, Allisson Francisco de Morais, Adriano de Oliveira Vigário, Maria Eduarda Flamini, Felipe Alves Mourato, Tsang Ing Ren, Marcos Antônio Dórea Machado   +9 more
wiley   +1 more source

An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction. [PDF]

Sci Rep
El-Behery H, Attia AF, Rezk NG.
europepmc   +1 more source

A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis

Journal of Applied Clinical Medical Physics, Volume 27, Issue 8, August 2026.
Abstract Background Accurate and rapid diagnosis of meniscal tears is crucial for effective management of sports‐related injuries and degenerative knee disorders. Magnetic resonance imaging (MRI) is widely used for meniscus evaluation; however, manual interpretation is time‐consuming and subject to inter‐observer variability.
Ebubekir Seyyarer, Hasan Genç, Faruk Ayata   +2 more
wiley   +1 more source

Hybrid deep learning and feature optimization approach for early detection of multiple sclerosis. [PDF]

Front Hum Neurosci
Anam N, S SB, Chowdhary CL.
europepmc   +1 more source

Automated Differentiation of Oral Red‐White Lesions: An Interpretable Deep Learning Approach Combining Ensemble Architectures and Saliency Maps

Clinical and Experimental Dental Research, Volume 12, Issue 4, August 2026.
ABSTRACT Introduction Oral Potentially Malignant Disorders (OPMDs), including Leukoplakia and Erythroplakia, carry significant risks of malignant transformation. Early differentiation from confounding inflammatory conditions like Oral Lichen Planus (OLP) and Candidiasis is critical yet challenging due to visual similarities.
Mahsa Koochaki, Amirreza Mousavie, Maryam Basirat, Amirreza Hendi, Hossein Sadr, Mojdeh Nazari   +5 more
wiley   +1 more source

Constructing a Predictive Model for STH and Schistosomiasis Classification From Microscopic Images. [PDF]

Biomed Res Int
Belachew E, Calpotura K, Adamu A, Getachew B.   +3 more
europepmc   +1 more source

Super‐Resolution of Planetary Images Based on Generative Adversarial Network

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Currently, satellite imagery serves as the primary means of observing terrestrial planets such as the Mars, the Moon, and Mercury. Enhancing the resolution and quality of these images can provide more detailed insights into planetary surfaces. However, improvements in image quality are often limited by the constraints of sensor technology and ...
Xiaoran Zhang, Yiran Wang, Miao Zhuo
wiley   +1 more source

High-Precision Marine Radar Object Detection Using Tiled Training and SAHI Enhanced YOLOv11-OBB. [PDF]

Sensors (Basel)
Külcü S.
europepmc   +1 more source

Artificial Intelligence‐Based Identification of Common Canine Skin Lesions From Clinical Images

Veterinary Dermatology, Volume 37, Issue 4, Page 471-477, August 2026.
Background: Accurate evaluation of skin lesions is an essential component of dermatological examination, yet it can be time‐consuming and subject to interobserver variability. While artificial intelligence (AI) models have shown reliability in diagnosing specific skin diseases, lesion‐level identification remains underexplored in veterinary dermatology.
Soh‐Yoon Kang, Yeong‐Hun Kang, Hyun‐Jung Kim, Eun‐A. Huh, Jong‐Soo Jeon, Hyun‐Su Kim, Min‐Sun Kim, Anisha Tiwari, Cheol‐Yong Hwang   +8 more
wiley   +1 more source

DeepInsight-Net: a CBAM-enhanced ResNet50 framework with focal loss for robust cervical cancer classification on multi-center datasets. [PDF]

Front Med (Lausanne)
Kılıç Eİ, Kılıç Ş.
europepmc   +1 more source