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Item type:Publication, Can deep learning wound segmentation algorithms developed for a dataset be effective for another dataset? A specific focus on diabetic foot ulcers(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)Diabetic foot ulcers (DFU) represent a severe complication, often resulting from poor glycemic control, neuropathy, peripheral vascular disease, or inadequate foot care. DFUs can lead to significant morbidity, including amputation and, in severe cases, can be fatal. Recently, advancements in computer vision technologies based on artificial intelligence (AI) have shown promise in DFU management. Particularly deep learning (DL) models such as U-Net and other models and techniques, were utilized to enhance wound segmentation accuracy. This research focuses on evaluating the generalization capabilities of DL models across different DFU datasets. Specifically, we investigated whether models trained on one dataset can be effective when utilized on another dataset, addressing the challenge of cross-dataset generalization. We employed 7 popular DL models, U-Net-VGG16, U-Net-EfficientNetV2S, ABANet, Ma-Net, LinkNet, DeepLabV3+, and Segment Anything Model (SAM), with 2 DFU datasets: FUSeg challenge and DFUC challenge. A total of 54 experiments were conducted plus 27 for SAM, involving training on one dataset, and testing on another, as well as training and testing on combined datasets. The results indicate substantial variability in segmentation performance when models trained on one dataset are tested on another, highlighting the influence of dataset characteristics on model generalization. The study underscores the importance of using diverse and comprehensive datasets to develop robust DL models for DFU segmentation and its generalization. This research contributes to the understanding of DL model performance in medical image segmentation and emphasizes the need for standardized datasets in improving DFU management through computer vision. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A deep learning approach to distance map generation applied to automatic fiber diameter computation from digital micrographs(Multidisciplinary Digital Publishing Institute (MDPI), 2024-09-01)Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of (Formula presented.) and a mean square error (MSE) of (Formula presented.), demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Diagnosis of oral cancer using deep learning algorithms(Universidad Politécnica Salesiana (Ecuador), 2024-07-01)Objective. The aim of this study was to use deep learning for the automatic diagnosis of oral cancer, employing images of the lips, mucosa, and oral cavity. A deep convolutional neural network (CNN) model, augmented with data, was proposed to enhance ora...2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A comparison between Deep Learning architectures for the assessment of breast tumor segmentation using VSI ultrasound protocol(IEEE, 2024-12-17)Automatic breast tumor ultrasound segmentation is one of the most critical components in the development of tools for breast cancer diagnosis. Several deep learning algorithms have been tested with public and private datasets but none of them has been de...6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Efficient Water Bottling Operations: A Continuous Improvement Analysis and Deep Learning-Driven Master Production Scheduler(LACCEI, 2024-01-01)We present a comprehensive solution aimed at enhancing water bottling operations by addressing production planning inefficiencies and order non-compliance, the MPS integrates forecast modelling, inventory control, and production management to streamline ...4