Weighted Loss Function Research Articles

Improved prediction of extreme ENSO events using an artificial neural network with weighted loss functions

The El Niño–Southern Oscillation (ENSO) causes a wide array of abnormal climates and extreme events, including severe droughts and floods, which have a major impact on humanity. With the development of artificial neural network techniques, various attempts are being made to predict ENSO more accurately. However, there are still limitations in accurately predicting ENSO beyond 6 months, especially for abnormal years with less frequent but greater impact, such as strong El Niño or La Niña, mainly due to insufficient and imbalanced training data. Here, we propose a new weighted loss function to improve ENSO prediction for abnormal years, in which the original (vanilla) loss function is multiplied by the weight function that relatively reduces the weight of high-frequency normal events. The new method applied to recurrent neural networks shows significant improvement in ENSO predictions for all lead times from 1 month to 12 months compared to using the vanilla loss function; in particular, the longer the prediction lead time, the greater the prediction improvement. This method can be applied to a variety of other extreme weather and climate events of low frequency but high impact.

End-to-end volumetric segmentation of white matter hyperintensities using deep learning

Background and objectivesReliable detection of white matter hyperintensities (WMH) is crucial for studying the impact of diffuse white-matter pathology on brain health and monitoring changes in WMH load over time. However, manual annotation of 3D high-dimensional neuroimages is laborious and can be prone to biases and errors in the annotation procedure. In this study, we evaluate the performance of deep learning (DL) segmentation tools and propose a novel volumetric segmentation model incorporating self-attention via a transformer-based architecture. Ultimately, we aim to evaluate diverse factors that influence WMH segmentation, aiming for a comprehensive analysis of the state-of-the-art algorithms in a broader context. MethodsWe trained state-of-the-art DL algorithms, and incorporated advanced attention mechanisms, using structural fluid-attenuated inversion recovery (FLAIR) image acquisitions. The anatomical MRI data utilized for model training was obtained from healthy individuals aged 62–70 years in the Live active Successful Aging (LISA) project. Given the potential sparsity of lesion volume among healthy aging individuals, we explored the impact of incorporating a weighted loss function and ensemble models. To assess the generalizability of the studied DL models, we applied the trained algorithm to an independent subset of data sourced from the MICCAI WMH challenge (MWSC). Notably, this subset had vastly different acquisition parameters compared to the LISA dataset used for training. ResultsConsistently, DL approaches exhibited commendable segmentation performance, achieving the level of inter-rater agreement comparable to expert performance, ensuring superior quality segmentation outcomes. On the out of sample dataset, the ensemble models exhibited the most outstanding performance. ConclusionsDL methods generally surpassed conventional approaches in our study. While all DL methods performed comparably, incorporating attention mechanisms could prove advantageous in future applications with a wider availability of training data. As expected, our experiments indicate that the use of ensemble-based models enables the superior generalization in out-of-distribution settings. We believe that introducing DL methods in the WHM annotation workflow in heathy aging cohorts is promising, not only for reducing the annotation time required, but also for eventually improving accuracy and robustness via incorporating the automatic segmentations in the evaluation procedure.

Enhancing Tomato Leaf Disease Detection through Generative Adversarial Networks and Genetic Algorithm based Convolutional Neural Network

In the agricultural sector, tomato leaf diseases signify a lot because they result in a lower crop yield and quality. Timely detection and classification of diseases help to ensure early interventions and effective treatment solutions. Nonetheless, the existing methods are confined by the dataset imbalance which affects class distribution negatively and thus results in poor models, especially for rare diseases. The research is designed to improve the capability of tomato leaf disease identification by investing a new deep-learning method beyond the challenge of imbalanced class distribution. By balancing the dataset, we aim to improve classification accuracy as we pay more attention to the under-represented classes. The proposed GAN-based method that combines the Weighted Loss Function to produce tomato leaf disease synthetic images is underrepresented. They improve the quality of the entire dataset, and the images from every class are now in a more balanced proportion. A CNN, which is the convolutional neural network, is trained for the classifier, with the weighted loss function as a part of the model. We used Genetic Algorithm (GA) for hyperparameter optimization of the CNN. It helps in emphasizing the learning process from the under-represented class. The suggested one will not only decrease the accuracy of tomato leaf disease detection but also increase it. Therefore, the synthetic images created by GAN enhance the dataset since the class distribution is brought to equilibrium. The incorporation of the weighted loss function into the model’s training process makes it very effective in handling with the class instability problem and consequently, the model can identify both common and rare diseases. From the outcomes of this study, it can be concluded that it is feasible to employ GAN and one loser weights function to solve the problem of class imbalance in tomato leaf disease recognition. A suggested approach that increases the model’s accuracy and reliability could be a good move to enhancing a reliable method of disease detection in the agricultural sector.

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively.

Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification.

In real classification scenarios, the number distribution of modeling samples is usually out of proportion. Most of the existing classification methods still face challenges in comprehensive model performance for imbalanced data. In this article, a novel theoretical framework is proposed that establishes a proportion coefficient independent of the number distribution of modeling samples and a general merge loss calculation method independent of class distribution. The loss calculation method of the imbalanced problem focuses on both the global and batch sample levels. Specifically, the loss function calculation introduces the true-positive rate (TPR) and the false-positive rate (FPR) to ensure the independence and balance of loss calculation for each class. Based on this, global and local loss weight coefficients are generated from the entire dataset and batch dataset for the multiclass classification problem, and a merge weight loss function is calculated after unifying the weight coefficient scale. Furthermore, the designed loss function is applied to different neural network models and datasets. The method shows better performance on imbalanced datasets than state-of-the-art methods.

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

Gradient Harmonization in Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) intends to transfer knowledge from a labeled source domain to an unlabeled target domain. Many current methods focus on learning feature representations that are both discriminative for classification and invariant across domains by simultaneously optimizing domain alignment and classification tasks. However, these methods often overlook a crucial challenge: the inherent conflict between these two tasks during gradient-based optimization. In this paper, we delve into this issue and introduce two effective solutions known as Gradient Harmonization, including GH and GH++, to mitigate the conflict between domain alignment and classification tasks. GH operates by altering the gradient angle between different tasks from an obtuse angle to an acute angle, thus resolving the conflict and trade-offing the two tasks in a coordinated manner. Yet, this would cause both tasks to deviate from their original optimization directions. We thus further propose an improved version, GH++, which adjusts the gradient angle between tasks from an obtuse angle to a vertical angle. This not only eliminates the conflict but also minimizes deviation from the original gradient directions. Finally, for optimization convenience and efficiency, we evolve the gradient harmonization strategies into a dynamically weighted loss function using an integral operator on the harmonized gradient. Notably, GH/GH++ are orthogonal to UDA and can be seamlessly integrated into most existing UDA models. Theoretical insights and experimental analyses demonstrate that the proposed approaches not only enhance popular UDA baselines but also improve recent state-of-the-art models.

Efficient click-based interactive segmentation for medical image with improved Plain-ViT.

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interactions stand out as an intuitive and straightforward approach compared to alternatives such as graffiti, bounding boxes, and extreme points. To improve the model's ability to interpret click-based interactions, we propose a comprehensive interactive segmentation framework that leverages an iterative weighted loss function based on user clicks. To enhance the segmentation capabilities of the Plain-ViT backbone, we introduce a Residual Multi-Headed Self-Attention encoder with hierarchical inputs and residual connections, offering multiple perspectives on the data. This innovative architecture leads to a remarkable improvement in segmentation model performance. In this research paper, we assess the robustness of our proposed framework using a self-compiled T2-MRI image dataset of the prostate and three publicly available datasets containing images of other organs. Our experimental results convincingly demonstrate that our segmentation model surpasses existing state-of-the-art methods. Furthermore, the incorporation of an iterative loss function training strategy significantly accelerates the model's convergence rate during interactions. In the prostate dataset, we achieved an impressive Intersection over Union (IoU) score of 88.11% and Number of Clicks(NoC) at 80% are 7.03 clicks.

Predicting multifunctional peptides based on a multi-scale ResNet model combined with channel attention mechanisms

<abstract> <p>Peptides are biomolecules composed of multiple amino acid residues connected by peptide bonds, which are widely involved in physiological and biochemical processes in organisms and exhibit diverse functions. In previous studies, the focus was primarily on single-functional peptides. However, research trends indicate that an increasing number of multifunctional peptides are being identified and discovered. To address this challenge, we proposed a deep learning method based on multi-scale ResNet as the backbone combined with a channel attention mechanism (called MSRC) for the identification of multifunctional peptides. Furthermore, the data imbalance problem was solved through the comprehensive use of online data augmentation and confidence-based weighted loss functions. Experimental results demonstrated that the proposed MSRC method achieved an accuracy of 0.688 with an absolute true rate of 0.619. Notably, in predicting minority class peptides such as AEP, AHIVP, and BBP, the MSRC model exhibited heightened sensitivity, showcasing its exceptional capability in addressing issues related to minority classes. By enhancing the precision in identifying and predicting multifunctional peptides, the MSRC method was poised to contribute significantly to advancements in drug discovery, disease treatment, and biotechnology.</p> </abstract>

RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions

Extracting river channels from remote sensing images is crucial for locating river water bodies and efficiently managing water resources, especially in cold and arid regions. The dynamic nature of river channels in these regions during the flood season necessitates a method that can finely delineate the edges of perennially changing river channels and accurately capture information about variable fine river branches. To address this need, we propose a river channel extraction method designed specifically for detecting fine river branches in remote sensing images within cold and arid regions. The method introduces a novel river attention U-shaped network structure (RAU-Net++), leveraging the rich convolutional features of VGG16 for effective feature extraction. For optimal feature extraction along channel edges and fine river branches, we incorporate a CBAM attention module into the upper sampling area at the end of the encoder. Additionally, a residual attention feature fusion module (RAFF) is embedded at each short jump connection in the dense jump connection. Dense skip connections play a crucial role in extracting detailed texture features from river channel features with varying receptive fields obtained during the downsampling process. The integration of the RAFF module mitigates the loss of river information, optimizing the extraction of lost river detail feature information in the original dense jump connection. This tightens the combination between the detailed texture features of the river and the high-level semantic features. To enhance network performance and reduce pixel-level segmentation errors in medium-resolution remote sensing imagery, we employ a weighted loss function comprising cross-entropy (CE) loss, dice loss, focal loss, and Jaccard loss. The RAU-Net++ demonstrates impressive performance metrics, with precision, IOU, recall, and F1 scores reaching 99.78%, 99.39%, 99.71%, and 99.75%, respectively. Meanwhile, both ED and ED′ of the RAU-Net++ are optimal, with values of 1.411 and 0.003, respectively. Moreover, its effectiveness has been validated on NWPU-RESISC45 datasets. Experimental results conclusively demonstrate the superiority of the proposed network over existing mainstream methods.

Factors associated with decision regret after bariatric surgery.

It is assumed that the individuals who undergo bariatric surgery will experience significant improvements in their health and overall well-being. However, it is yet to be examined whether these individuals may also experience subsequent decision regret. The level of regret regarding the choice to undergo bariatric surgery was assessed 1 year after bariatric surgery using the Decision Regret Scale (DRS). Associations of regret with patient characteristics, complications, weight loss and quality of life (BODY-Q) were investigated using linear regression analyses. In total, 115 patients completed the DRS (92% underwent Roux-en-Y gastric bypass Roux-en-Y gastric bypass and 8% underwent sleeve gastrectomy (SG)). Two out of 115 patients indicated absolute regret about their decision to undergo bariatric surgery because of insufficient weight loss and complications. The median decision regret score was zero (range 0-80). Most patients experienced no decision regret (50.4%), followed by mild regret (34.8%) and moderate to strong regret (14.8%). Higher levels of regret were associated with having osteo-articular disorders, gastro-oesophageal reflux disease or a history of psychiatric disorders at baseline. Patients with mild regret demonstrated significantly more weight loss and better psychological function. Major surgical complications were not associated with increased decision regret. Only two out of 115 patients (1.7%) indicated absolute regret about their decision to undergo bariatric surgery, and 15% reported moderate-to-strong regret according to the results of the DRS. These findings should be considered when providing pre-operative counselling and could assist patients in their decision-making process.

Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme Precipitation Over Southern China Based on a Deep Learning Method

AbstractThe reliable Subseasonal‐to‐Seasonal (S2S) forecast of precipitation, particularly extreme precipitation, is critical for disaster prevention and mitigation, which however remains a great challenge for mission agencies and research communities. In this study, a deep learning method based on U‐Net with additional atmospheric factor forecasts included is proposed to improve S2S quantitative forecasts of summer precipitation over Southern China. The weighted loss function integrated by mean square error and threat score is introduced to capture extreme precipitation more precisely. Generally, the U‐Net model shows promising results in both general statistics and extreme events. Predictor importance analyses show that the U‐Net forecast skills at the 1‐week lead time mainly arise from synchronous precipitation forecasts, but the contributions made by atmospheric factor forecasts rise rapidly with increasing lead times. Therefore, the channel combining numerical weather prediction model and deep learning framework is demonstrated promising in S2S precipitation forecasts.

CSRL-Net: contextual self-rasterization learning network with joint weight loss for remote sensing image semantic segmentation

ABSTRACTSemantic segmentation plays a vital role in the intelligent comprehension of remote sensing images (RSIs). However, research on semantic segmentation of RSIs still faces the following challenges: 1) The complexity of ground object structures, including variations in scale and shading environments, poses difficulties for current methods in capturing global context. 2) In long-tail distributed remote sensing data, the scarcity of tail classes makes it difficult for their features to be effectively learned, as features of head classes often overshadow them. To address these issues, we propose a contextual self-rasterization learning network (CSRL-Net) for the semantic segmentation of RSIs. Our approach comprises the following two key components. Firstly, a grid context perception mechanism is developed to collaboratively establish context dependencies within and among multi-scale grids, capturing long-range spatial correlations. Secondly, a joint weight loss function is designed to convert the prior knowledge into weight factors. This loss function combines re-weighting and logit adjustment, giving more attention to tail classes and integrally balancing learning bias. To evaluate the effectiveness of our proposed method, we apply it to the Potsdam, Vaihingen, and GID datasets and compare its performance with current advanced models. Experimental results demonstrate that our method achieves excellent performance in terms of MIoU, mean F1 and OA, with improvements ranging from 0.364% to 1.764% compared to the 17 comparison models. Notably, the proposed joint weight loss significantly improves IoU and F1 for tail classes, resulting in increases of 2.909% (IoU) and 2.085% (F1) on the Vaihingen dataset and 4.697% (IoU) and 4.043% (F1) on the GID dataset.

Integrating a non-gridded space representation into a graph neural networks model for citywide short-term crash risk prediction

Current applications of Graph Neural Networks in citywide short-term crash risk prediction have been limited by a gridded representation of space, which restricts the network’s capability to effectively capture the spatial and temporal dependency of crash occurrences. In addition, a grided representation does not match most geographic units used for administrative purposes, limiting the use of crash risk predictions by practitioners. This paper applies a gated localised diffusion graph neural network (GLDNet) model to compare the use of two alternative geographic units, Mesh Block (MB) and grid, to forecast locations where crashes are likely to occur in a future time window. The GLDNet relies on a graph-based representation of geographic units and a weighted loss function to address the sparsity of crash occurrences. The tests are performed using crash data from the City of Melbourne, Australia, over a period of one year. The predictions are made at six-hour intervals, and the results show that the GLDNet consistently outperforms baseline methods, with differences in prediction accuracy from 10% to 21% in relation to historical average and benchmark deep learning models. In terms of geographic units, the MB-based GLDNet performed better than its grid counterpart, with differences in prediction accuracy of up to 12.3%. The better performance stems from the underlying information attached to the MB units (i.e., land use) and the network properties (i.e., degree of centrality), which enhance the GLDNet capability to identify crash risk in both central and peripherical areas. Regarding its applicability, the MB-based GLDNet directly integrates with other data sources, which provides contextual information about crash hotspots that helps decision-makers develop police patrolling and rescuing strategies.

Fair patient model: Mitigating bias in the patient representation learned from the electronic health records

ObjectiveTo pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. MethodsWe defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. ResultsFPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. ConclusionFPM is a novel method to pre-train fair and unbiased patient representations from the EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where fairness is important.

Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model

Physics-informed neural networks (PINN) architectures have been recently explored to accelerate hemodynamics simulations by leveraging mathematical models for blood flow and empirical data. In this paper, a variable separated physics-informed neural networks based on adaptive weighted loss functions (AW-vsPINN) is developed for blood flow model in arteries. In particular, sub-neural networks are proposed to separately predict the unknown scalar state variables by sharing the same input layer. The AW-vsPINN adaptively adjusts the weights of loss terms by the minmax algorithm, which will be updated synchronously along with the network parameters and can balance the contributions of different loss terms during training. The two-stage optimization is implemented to train the neural networks. Specifically, the Adam optimizer is iterated for initial steps with the learning rate generated by the inverse time decay scheduler, and then the L-BFGS optimizer continues to train until the loss converges. Numerical results illustrate that the AW-vsPINN can remarkably improve prediction accuracy and enhance the ability of generalization compared to the conventional PINN. The proposed AW-vsPINN framework has high potential in predicting the blood flow information in cardiovascular disease.

Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

AbstractIn many fields, like aspect category detection (ACD) in aspect‐based sentiment analysis, it is necessary to label each instance with more than one label at the same time. This study tackles the multilabel classification problem in the ACD task for the Arabic language. For this purpose, we used Arabic hotel reviews from the SemEval‐2016 dataset, comprising 13,113 annotated tuples provided for training (10,509) and testing (2,604). To extract valuable information, we first propose specific data preprocessing. Then, we suggest using the dynamic weighted loss function and a data augmentation method to fix the problem with this dataset's imbalance. Using two possible approaches, we develop new ways to find different categories of things in a review sentence. The first is based on classifier chains using machine learning models. The second is based on transfer learning using pretrained AraBERT fine‐tuning for contextual representation. Our findings show that both approaches outperformed the related works for ACD on the Arabic SemEval‐2016. Moreover, we observed that AraBERT fine‐tuning performed much better and achieved a promising ‐score of .

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity images widely regarded as the most reliable diagnostic tool available. Employing deep learning techniques for automated stroke lesion segmentation can offer valuable insights into the precise location and extent of affected tissue, enabling medical professionals to effectively evaluate treatment risks and make informed assessments. In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. To tackle the class imbalance challenge, we employed a strategy of using small patches with balanced sampling during training, along with a dynamically weighted loss function that incorporates f1-score and IOU-score (Intersection over Union). Furthermore, the 3D U-Net architecture is used to generate predictions for complete patches, employing a high degree of overlap between patches to minimize the requirement for subsequent post-processing steps. The 3D U-Net model, utilizing ResnetV2 as the pre-trained encoder for IOU-score and Seresnext101 for f1-score, stands as the leading state-of-the-art (SOTA) model for segmentation tasks. However, recent research has introduced a novel model that surpasses these metrics and demonstrates superior performance compared to other backbone architectures. The f1-score and IOU-score were computed for various backbones, with Seresnext101 achieving the highest f1-score and ResnetV2 performing the highest IOU-score. These calculations were conducted using a threshold value of 0.5. This research proposes a valuable model based on transfer learning for the classification of brain diseases in MRI scans. The achieved f1-score using the recommended classifiers demonstrates the effectiveness of the approach employed in this study. The findings indicate that Seresnext101 attains the highest f1-score of 0.94226, while ResnetV2 achieves the best IOU-score of 0.88342, making it the preferred architecture for segmentation methods. Furthermore, the study presents experimental results of the 3D U-Net model applied to brain stroke lesion segmentation, suggesting prospects for researchers interested in segmenting brain strokes and enhancing 3D U-Net models.

Imbalanced learning for wind turbine blade icing detection via spatio-temporal attention model with a self-adaptive weight loss function

Accurate blade icing detection of wind turbines is of great significance to avoid secondary damages or accidents. Recently, data-driven methods have attracted increasing attention due to the availability of supervisory control and data acquisition (SCADA) data with their low-cost and high volume. However, SCADA data usually present complex characteristics of high dimensionality and strong spatio-temporal correlations among different sensor variables and severe class imbalance. It is challenging to extract effective features for accurate and reliable icing detection from imbalanced SCADA data. To address these challenges, we propose an imbalanced learning method for blade icing detection. First, we propose a spatio-temporal attention model to extract and assign weights to temporal-spatial features based on their contributions to blade icing detection. Then, to address the class imbalance issue, we design a self-adaptive weight (SAW) loss function to divide SCADA data into multiple batches and then classify them during model training. Unlike conventional loss functions, our SAW loss can adaptively assign weights to each data category according to their numbers in the divided batches. The effectiveness of the proposed method is verified on real SCADA datasets. The results demonstrate our designed spatio-temporal attention and SAW loss achieved better classification results and superior self-adaptability.

Faecal metabolome responses to an altered dietary protein:carbohydrate ratio in adult dogs

High-protein diets may aid weight loss and weight maintenance programs in both humans and dogs, although the effect of dietary protein levels on gut metabolism and functionality has not been studied in depth. The current study aimed to investigate the effect of an altered dietary protein:carbohydrate ratio on gut function in adult dogs by means of faecal metabolomic fingerprinting. More specifically, functional metabolic differences in dogs fed a high-protein/low-carbohydrate (HPLC) vs. low-protein/high-carbohydrate (LPHC) diet were studied by equally allocating twelve clinically healthy (6 lean and 6 obese) Beagles into two groups in a cross-over design, with each group receiving two isocaloric diets for four weeks. The faecal metabolome revealed that different protein:carbohydrate ratio can influence host and/or gut microbiome metabolism and function, while no effect was observed on the body condition. Targeted analysis demonstrated that the HPLC diet significantly increased the concentration of indole, spermidine, and pipecolinic acid and decreased the concentration of azelaic acid, D-fructose, mannose, and galactose (p < 0.05). Multivariate modelling (OPLS-DA) of the untargeted faecal metabolome revealed distinctly different metabolomic profiles following the HPLC vs. LPHC diet, with 18 altered pathways. The HPLC diet influenced amino acid and lipid metabolism, potentially promoting weight loss and immune function, whereas the LPHC diet affected carbohydrate fermentation and may promote anti-oxidative function.