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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 PDFEffect of ultrasound combined with edible coating based on guar gum on the physicochemical quality and texture of fried zucchini slices.
J Food Sci
January 2025
Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, Iran.
Edible coating (EC) can reduce excessive oil absorption in deep-fat fried food products. Ultrasound is an efficient pretreatment to preserve the quality characteristics of fried samples. The impact of guar gum based EC and sonication on the quality parameters of fried zucchini slices was investigated.
View Article and Find Full Text PDFMultispectral 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 PDFPerfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture.
Int J Comput Assist Radiol Surg
January 2025
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Purpose: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).
View Article and Find Full Text PDFscHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.
Brief Bioinform
November 2024
School of Software, Shandong University, No. 1500, Shunhua Road, Hi-Tech Industrial Development Zone, Jinan 250100, Shandong, China.
Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation.
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