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Hybrid Data Augmentation Strategies for Robust Deep Learning Classification of Corneal Topographic MapTopographic Map.
Biomed Phys Eng Express
January 2025
National School of Electronics and Telecommunication of Sfax, Sfax rte mahdia, sfax, sfax, 3012, TUNISIA.
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification.
View Article and Find Full Text PDFA deep learning tissue classifier based on differential Co-expression genes predicts the pregnancy outcomes of cattle.
Biol Reprod
January 2025
Inner Mongolia SK·Xing Animal Breeding and Breeding Biotechnology Research Institute Co., Ltd, Hohhot 011517, China.
Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant).
View Article and Find Full Text PDFEnhancing panoramic dental imaging with AI-driven arch surface fitting: Achieving improved clarity and accuracy through an optimal reconstruction zone.
Dentomaxillofac Radiol
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, 50612, Korea.
Objectives: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.
Methods: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.
Multispectral imaging-based detection of apple bruises using segmentation network and classification model.
J Food Sci
January 2025
College of Electronics and Engineering, Heilongjiang University, Harbin, China.
Bruises can affect the appearance and nutritional value of apples and cause economic losses. Therefore, the accurate detection of bruise levels and bruise time of apples is crucial. In this paper, we proposed a method that combines a self-designed multispectral imaging system with deep learning to accurately detect the level and time of bruising on apples.
View Article and Find Full Text PDFPerformance analysis of image retrieval system using deep learning techniques.
Network
January 2025
Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, India.
The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results.
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