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Item type:Publication, Non-Contact Respiratory Rate Estimation in Newborns During Quiet Sleep Using Video Magnification Techniques and a 3D Convolutional Neural Network(IEEE, 2024); ; Cabon, SandieIn this paper, we present a new non-contact strategy to estimate the respiratory rate (RR) in a neonatal intensive care unit (NICU) based on the Eulerian motion video magnification technique and a 3D Convolutional Neural Network (3D CNN). The magnification procedure was carried out using the Hermite decomposition. The RR is estimated using a 3D CNN and a region of interest (ROI) detected manually. We have tested the method on 8 infants in NICU during quiet sleep. A contact respiratory signal is acquired synchronously to the videos to compute the RR as reference for training the CNN. To compare the performance of the method, we compute the Mean Absolute Error, the Root Mean Squared Error and metrics from the Bland and Altman analysis to investigate the agreement of the method with respect to the respiratory signal reference. The proposed solution shows an agreement with respect to the reference of 95% and root mean squared error of 2.88. ©The authors ©IEEE.11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimal Dataset Size for Fine-Tuning sEMG-Based Hand Gesture Recognition in Rehabilitation Prosthesis(IEEE, 2024); ; ; Franco-Gaona, ErickSurface electromyography (sEMG) has become a vital tool for controlling prostheses and rehabilitation using hand gesture recognition. However, the process of fine-tuning machine learning models to individual users often requires considerable amounts of data, which can be challenging to obtain due to user fatigue and discomfort. This work investigates the optimal dataset size needed for fine-tuning a pretrained Convolutional Neural Network (CNN) model for hand gesture recognition, using the NinaPro DB2 dataset. Our results show that training on just a third of the dataset achieves over 90% accuracy, highlighting a significant reduction in the data requirements compared to traditional methods. This approach can minimize the burden of data collection on users, making sEMG-based rehabilitation devices more practical and accessible. ©The authors ©IEEE8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy(MDPI, 2024); ; Lopez-Figueroa, AlbertoNowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images. ©MDPIScopus© Citations 1 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Vision-Based Analysis on Leaves of Tomato Crops for Classifying Nutrient Deficiency using Convolutional Neural Networks(2020); ; Brieva, JorgeTomato crops are one of the most important agricultural products at economic level in the world. However, the quality of the tomato fruits is highly dependent to the growing conditions such as the nutrients. One of consequences of the latter during tomato harvesting is nutrient deficiency. Manually, it is possible to anticipate the lack of primary nutrients (i.e. nitrogen, phosphorus and potassium) by looking the appearance of the leaves in tomato plants. Thus, this paper presents a supervised vision-based monitoring system for detecting nutrients deficiencies in tomato crops by taking images from the leaves of the plants. It uses a Convolutional Neural Network (CNN) to recognize and classify the type of nutrient that is deficient in the plants. First, we created a data set of images of leaves of tomato plants showing different symptoms due to the nutrient deficiency. Then, we trained a suitable CNN-model with our images and other augmented data. Experimental results showed that our CNN-model can achieve 86.57% of accuracy. We anticipate the implementation of our proposal for future precision agriculture applications such as automated nutrient level monitoring and control in tomato crops. © 2020 IEEE.Scopus© Citations 23 15 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards the Distributed Wound Treatment Optimization Method for Training CNN Models: Analysis on the MNIST DatasetConvolutional neural network (CNN) is a prominent algorithm in Deep Learning methods. CNN architectures have been used successfully to solve various problems in image processing, for example, segmentation, classification, and enhancement task. However, automatic search for suitable architectures and training parameters remain an open area of research, where metaheuristic algorithms have been used to fine-tuning the hyperparameters and learning parameters. This work presents a bio-inspired distributed strategy based on Wound Treatment optimization (WTO) for training the learning parameters of a LenNet CNN model fast and accurate. The proposed method was evaluated over the popular benchmark dataset MNIST for handwritten digit recognition. Experimental results showed an improvement of 36.87% in training time using the distributed WTO method compared to the baseline with a single learning agent, and the accuracy increases 4.69% more using the proposed method in contrast with the baseline. As this is a preliminary study towards the distributed WTO method for training CNN models, we anticipate this approach can be used in robotics, multi-agent systems, federated learning, complex optimization problems, and many others, where an optimization task is required to be solved fast and accurate. © 2023 IEEE.Scopus© Citations 2 9 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized ArchitectureThe breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is (Formula presented.) with and agreement with respect of the reference of ≈99%. © 2023 by the authors.Scopus© Citations 8 51 1