Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

Application of MinION Long-Read Sequencer for Semi-targeted Detection of Foodborne Pathogens.

Foodborne pathogens remain a serious health issue in developed and developing countries. Safeness of food products has been assured for years with culture-based microbiological methods; however, these present several limitations such as turnaround time and extensive hands-on work, which have been typically address taking advantage of DNA-based methods such as real-time PCR (qPCR). These, and other similar techniques, are targeted assays, meaning that they are directed for the specific detection of one specific microbe. Even though reliable, this approach suffers from an important limitation that unless specific assays are design for every single pathogen potentially present, foods may be considered erroneously safe. To address this problem, next-generation sequencing (NGS) can be used as this is a nontargeted method; thus it has the capacity to detect every potential threat present. In this chapter, a protocol for the simultaneous detection and preliminary serotyping of Salmonella enterica serovar Enteritidis, Salmonella enterica serovar Typhimurium, Listeria monocytogenes, and Escherichia coli O157:H7 is described.

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

[Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

[Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals].

Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

A novel kit for enrichment of fecal helminth eggs.

We developed a new concentration kit, called the ParaEgg (PE), for easy detection trematode eggs from fecal samples in endemic areas of clonorchiasis and metagonimiasis in Korea. To create a standard of detection efficiency, 120 fecal samples were examined using the water-ether concentration method (WECM). The PE kit and Mini ParaSep (PS) kit were used to compare the detection sensitivity of 100 egg-positive and 20 egg-negative samples in WECM. Additionally, stool samples, which were intentionally spiked with 10, 20, and 30 Clonorchis sinensis eggs, were evaluated to assess the sensitivity in lowinfection cases. The PE and PS kits showed detection rates of 100% and 92%, respectively, from 100 egg-positive samples in WECM. Meanwhile, eggs were detected in 3 (PE) and 2 (PS) out of 20 egg-negative samples in WECM. The PE kit detected the highest number of eggs per gram of feces (727 on average), followed by the WECM (524) and PS kit (432). In fecal samples that were intentionally spiked with 10, 20, and 30 C. sinensis eggs, PE only detected eggs 2 out of 5 samples in 10 eggs spiked (40%), and the detection rates were 80% and 100%, respectively. The PE kit enabled a more accurate identification of trematode eggs because of the clearance of small fecal debris in the microscopic field. In conclusion, the PE kit is obviously helpful to detect and identify trematode eggs in stool examinations especially in endemic areas of clonorchiasis and metagonimiasis.

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search.

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

Analysis of structural stability of Ar ion-implanted Pd/Zr/Pd/Ti/Pd multilayer stack in hydrogen annealing environment.

The influence of 150 keV argon ions at fluences in the range of 1 × 1012-1 × 1016 ions/cm2 on the stability of the multilayer stack Pd/Zr/Pd/Ti/Pd thin films system, deposited on Ti and Ti6Al4V substrates, under thermal annealing in an H2 environment was investigated. For samples deposited on Ti substrate, RBS revealed structural instability that increases with fluence. This is evidenced by a decrease in the intensity of layers accompanied by increased consumption of the Pd layers. This effect led to the initial individual layers becoming one compound layer and the formation of a new Ti-O-Pd layer, indicating a complete intermixing of layers at 1 × 1016 ions/cm2. However, for the samples deposited on Ti6Al4V substrate, the Pd layers could still be identified and resolved, indicating an incomplete intermixing of layers. XRD revealed the structural transformation of layers via an intermixing process resulting in the formation of two new phases, TiH2 and ZrH2, classified as face-centered tetragonal (FCT) crystal structures. ERDA confirmed the presence of hydrides in the system indicating the absorption of H into the system to a maximum H amount of ∼5.2 at.%, at higher fluence, for the same multilayer stack deposited on both substrates.

A real-time vehicle safety system by concurrent object detection and head pose estimation via stereo vision.

A considerable number of vehicular accidents occur in low-millage zones like school streets, neighborhoods, and parking lots, among others. Therefore, the proposed work aims to provide a novel ADAS system to warn about dangerous scenarios by analyzing the driver's attention and the corresponding distances between the vehicle and the detected object on the road. This approach is made possible by concurrent Head Pose Estimation (HPE) and Object/Pedestrian Detection. Both approaches have shown independently their viable application in the automotive industry to decrease the number of vehicle collisions. The proposed system takes advantage of stereo vision characteristics for HPE by enabling the computation of the Euler Angles with a low average error for classifying the driver's attention on the road using neural networks. For Object Detection, stereo vision is used to detect the distance between the vehicle and the approaching object; this is made with a state-of-the-art algorithm known as YOLO-R and a fast template matching technique known as SoRA that provides lower processing times. The result is an ADAS system designed to ensure adequate braking time, considering the driver's attention on the road and the distances to objects.

Depth detection limit of a fluorescent object in tissue-like medium with background emission in continuous-wave measurements: a phantom study.

Significance: Although the depth detection limit of fluorescence objects in tissue has been studied, reports with a model including noise statistics for designing the optimum measurement configuration are missing. We demonstrate a variance analysis of the depth detection limit toward clinical applications such as noninvasively assessing the risk of aspiration. Aim: It is essential to analyze how the depth detection limit of the fluorescence object in a strong scattering medium depends on the measurement configuration to optimize the configuration. We aim to evaluate the depth detection limit from theoretical analysis and phantom experiments and discuss the source-detector distance that maximizes this limit. Approach: Experiments for detecting a fluorescent object in a biological tissue-mimicking phantom of ground beef with background emission were conducted using continuous wave fluorescence measurements with a point source-detector scheme. The results were analyzed using a model based on the photon diffusion equations. Then, variance analysis of the signal fluctuation was introduced. Results: The model explained the measured fluorescence intensities and their fluctuations well. The variance analysis showed that the depth detection limit in the presence of ambient light increased with the decrease in the source-detector distance, and the optimum distance was in the range of 10 to 15 mm. The depth detection limit was found to be ∼ 30 mm with this optimum distance for the phantom. Conclusions: The presented analysis provides a guide for the optimum design of the measurement configuration for detecting fluorescence objects in clinical applications.

How to detect fake online physician reviews: A deep learning approach.

Objective: The COVID-19 pandemic has spurred an increased interest in online healthcare and a surge in usage of online healthcare platforms, leading to a proliferation of user-generated online physician reviews. Yet, distinguishing between genuine and fake reviews poses a significant challenge. This study aims to address the challenges delineated above by developing a reliable and effective fake review detection model leveraging deep learning approaches based on a fake review dataset tailored to the context of Chinese online medical platforms. Methods: Inspired by prior research, this paper adopts a crowdsourcing approach to assemble the fake review dataset for Chinese online medical platforms. To develop the fake review detection models, classical machine learning models, along with deep learning models such as Convolutional Neural Network and Bidirectional Encoder Representations from Transformers, were applied. Results: Our experimental deep learning model exhibited superior performance in identifying fake reviews on online medical platforms, achieving a precision of 98.36% and an F2-Score of 97.97%. Compared to the traditional machine learning models (i.e., logistic regression, support vector machine, random forest, ridge regression), this represents an 8.16% enhancement in precision and a 7.7% increase in F2-Score. Conclusion: Overall, this study provides a valuable contribution toward the development of an effective fake physician review detection model for online medical platforms.

Development and evaluation of a rapid visual loop-mediated isothermal amplification assay for the tcdA gene in Clostridioides difficile detection.

Background: The tcdA gene codes for an important toxin produced by Clostridioides difficile (C. difficile), but there is currently no simple and cost-effective method of detecting it. This article establishes and validates a rapid and visual loop-mediated isothermal amplification (LAMP) assay for the detection of the tcdA gene. Methods: Three sets of primers were designed and optimized to amplify the tcdA gene in C. difficile using a LAMP assay. To evaluate the specificity of the LAMP assay, C. difficile VPI10463 was used as a positive control, while 26 pathogenic bacterial strains lacking the tcdA gene and distilled water were utilized as negative controls. For sensitivity analysis, the LAMP assay was compared to PCR using ten-fold serial dilutions of DNA from C. difficile VPI10463, ranging from 207 ng/µl to 0.000207 pg/µl. The tcdA gene of C.difficile was detected in 164 stool specimens using both LAMP and polymerase chain reaction (PCR). Positive and negative results were distinguished using real-time monitoring of turbidity and chromogenic reaction. Results: At a temperature of 66 °C, the target DNA was successfully amplified with a set of primers designated, and visualized within 60 min. Under the same conditions, the target DNA was not amplified with the tcdA12 primers for 26 pathogenic bacterial strains that do not carry the tcdA gene. The detection limit of LAMP was 20.700 pg/µl, which was 10 times more sensitive than that of conventional PCR. The detection rate of tcdA in 164 stool specimens using the LAMP method was 17% (28/164), significantly higher than the 10% (16/164) detection rate of the PCR method (X2 = 47, p < 0.01). Conclusion: LAMP method is an effective technique for the rapid and visual detection of the tcdA gene of C. difficile, and shows potential advantages over PCR in terms of speed, simplicity, and sensitivity. The tcdA-LAMP assay is particularly suitable for medical diagnostic environments with limited resources and is a promising diagnostic strategy for the screening and detection of C. difficile infection in populations at high risk.

Implementation of Artificial Intelligence-Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study.

BACKGROUND: Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss. OBJECTIVE: We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center. METHODS: We prospectively recruited adult patients with diabetes at the Centre hospitalier de l'Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings. RESULTS: This study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes. CONCLUSIONS: Our study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.

Perpetual step-like restructuring of hippocampal circuit dynamics.

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies.

Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania.

Agriculture is considered the backbone of Tanzania's economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.

Perceptions of multi-cancer early detection tests among communities facing barriers to health care.

Marginalized racial and ethnic groups and rural and lower income communities experience significant cancer inequities. Blood-based multi-cancer early detection tests (MCEDs) provide a simple and less invasive method to screen for multiple cancers at a single access point and may be an important strategy to reduce cancer inequities. In this qualitative study, we explored barriers and facilitators to MCED adoption among communities facing health care access barriers in Alaska, California, and Oregon. We used reflexive thematic analysis to analyze general barriers to cancer screening, MCED-specific barriers, facilitators of MCED adoption, and MCED communication strategies. We found barriers and facilitators to MCED adoption across 4 levels of the social-ecological model: (1) individual, (2) interpersonal, (3) health care system, and (4) societal. These included adverse psychological impacts, positive perceptions of MCEDs, information and knowledge about cancer screening, the quality of the patient-provider relationship, a lack of health care system trustworthiness, logistical accessibility, patient supports, and financial accessibility. Optimal MCED communication strategies included information spread through the medical environment and the community. These findings underscore the importance of understanding and addressing the multilevel factors that may influence MCED adoption among communities facing health care access barriers to advance health equity.

Bioelectrochemical biosensors for water quality assessment and wastewater monitoring.

Bioelectrochemical biosensors offer a promising approach for real-time monitoring of industrial bioprocesses. Many bioelectrochemical biosensors do not require additional labelling reagents for target molecules. This simplifies the monitoring process, reduces costs, and minimizes potential contamination risks. Advancements in materials science and microfabrication technologies are paving the way for smaller, more portable bioelectrochemical biosensors. This opens doors for integration into existing bioprocessing equipment and facilitates on-site, real-time monitoring capabilities. Biosensors can be designed to detect specific heavy metals such as lead, mercury, or chromium in wastewater. Early detection allows for the implementation of appropriate removal techniques before they reach the environment. Despite these challenges, bioelectrochemical biosensors offer a significant leap forward in wastewater monitoring. As research continues to improve their robustness, selectivity, and cost-effectiveness, they have the potential to become a cornerstone of efficient and sustainable wastewater treatment practices.

Correlates of intention-to-attend and confirmed cervical screening attendance during the COVID-19 pandemic in Australia: Findings from Compass-PLUS, a prospective cohort study.

Objective: The coronavirus pandemic impacted health-seeking behaviour and access to primary care in Australia. We investigated factors associated with intention-to-attend and attendance of cervical screening during the pandemic, mainly in Victoria, Australia. Methods: We used questionnaire and attendance data (Aug 2020-Nov 2022) from Compass-PLUS, a sub-study of the Compass randomized-controlled trial of Human Papillomavirus-based vs cytology-based screening. Data was restricted to the HPV-screening arm for comparability to the national program. We investigated associations overall and for younger (25-39 years) and older (≥40 years) cohorts, between intention-to-attend/attendance, and socio-demographics, anxiety-related scores, and agreement with beliefs about screening during the pandemic (e.g. importance of screening, increased workload, working from home, risk of infection). Results: Among 2,226 participants, positive intention to attend screening was more likely among those with a family history of cancer (p = 0.030) or living outside major cities (p = 0.024). Increased attendance was associated with increasing age (p < 0.001), prior regular cervical screening history [adjusted relative risk (aRR) for 2 screens in 6 years vs none: 1.23 (95 %CI 1.09,1.40); p < 0.001], and part-time employment or retirement compared to full-time employment [aRR:1.08 (1.02,1.14); aRR:1.12 (1.03, 1.22); respectively]. Lower attendance was related to increased agreement with statements indicating screening de-prioritisation (p-trend < 0.05) and higher recent anxiety, specifically in the older cohort (p-trend = 0.002). Conclusions: Reduced priority of screening and heightened recent anxiety may partly explain indications of lower-than-expected cervical screening rates during the pandemic. It is important that catch-up of missed HPV screens is performed to prevent a possible increase in cancer diagnoses in the long term.

Rapid detection of avian leukemia virus using CRISPR/Cas13a based lateral flow dipstick.

Avian leukemia virus (ALV) is one of the main pathogens of poultry tumor diseases, and has caused significant economic losses to the poultry industry since its discovery. Therefore, establishing a rapid detection method is essential to effectively prevent and control the spread of ALV. In this study, specific CRISPR RNA (crRNA) and recombinase-aided amplification (RAA) primers with T7 promoter were designed based on the relatively conserved sequence of avian leukemia virus. When crRNA recognized the target sequence, Cas13a protein was activated to cut the reporting probes, and then the detection results were read by using lateral flow dipstick (LFD). The RAA-CRISPR/Cas13a-LFD reaction system was constructed. The RAA amplification time, Cas13a protein concentration, crRNA concentration and CRISPR reaction time were optimized to evaluate the specificity, sensitivity and reproducibility of the system. Finally, RAA-CRISPR/Cas13a-LFD method was compared with Polymerase chain reaction (PCR)-Agarose electrophoresis method and qPCR method in the detection of clinical samples, and the reliability of RAA-CRISPR/Cas13a-LFD method was evaluated. The results showed that the RAA-CRISPR/Cas13a-LFD method could effectively amplify the target gene at 37°C for 40 min, and the test results could be determined by LFD visual observation. The method had good specificity and no cross-reaction with Marek's disease virus (MDV), Fowl adenovirus (FAdV), Infectious bursal disease virus (IBDV), Newcastle disease virus (NDV), Infectious laryngotracheitis virus (ILTV), and Infectious bronchitis virus (IBV). The minimum detection limit of the method was 100 copies/µL, and it had good repeatability and stability. The coincidence rate of clinical detection reached 97.69% and 99.23%. In summary, this study established a simple, efficient, accurate and visualized ALV detection method, which can be used for the prevention and rapid clinical diagnosis of avian leukosis (AL).

A multi-scale attention mechanism for detecting defects in leather fabrics.

Defect detection is critical to industrial quality control in leather production engineering. The various sizes and locations of defects in leather, as well as significant differences within the same class and indistinctive variations between different classes of defects, contribute to the complexity of the problem. To address this challenge, we propose a Multi-Layer Residual Convolutional Attention (MLRCA) approach. MLRCA enhances its ability to capture both intra-class and inter-class differences by enhancing the semantic feature representation in the backbone network. To improve multiscale fusion effects, we also incorporate the MLRCA module into the feature pyramid network (FPN) and propose a new multi-layer residual convolution attention feature pyramid network (ML-FPN). This approach enables more accurate identification of leather defects at a more detailed level by selectively capturing contextual information from different domains. We then implement the Side-Aware Boundary Localization (SABL) detection head, which accurately locates defects and helps the network distinguish between similar defect categories for more precise positioning. To validate the effectiveness of our approach, we conducted ablation experiments on the created leather dataset. Comparative experiments demonstrate the excellent capability of our model to detect minor defects. The model achieved 83.4, 89.7, and 85.6 for the AP, AP50, and AP75 evaluation metrics. In addition, the model achieves 71.3, 89.9, and 88.9 for APS, APM, and APL. Our approach has been confirmed feasible through experimentation and provides new insights for automated leather defect detection methods.