2,240 research outputs found
Convolutional neural networks for the automatic control of consumables for analytical laboratories
No full textIn recent years, the need for advanced systems and technologies for industrial process optimization using computer vision and artificial intelligence (AI) techniques has become increasingly pervasive. The specific focus of this study is to introduce an AI-based monitoring system within a production chain involved in manufacturing plastic consumables for analytical laboratories, specifically targeting the control of vials containing an anticoagulant substance. Currently, the inspection process relies on manual visual inspection conducted on a sample basis, resulting in the potential discarding of entire production batches if the absence of the anticoagulant substance is detected in a single vial. To overcome the inefficiency of the manual system, a comprehensive method is proposed to verify the presence of the anticoagulant substance in all produced vials, leveraging advanced computer vision and AI techniques. This innovative monitoring system offers promising solutions for enhancing industrial processes by enabling accurate and real-time monitoring. Specifically, we present our model and some preliminary results showing the potentiality of the proposed approach.
Keywords: automatic monitoring, green economy, deep learning, convolutional neural networks
1. Introduction
In recent years, the application of computer vision and artificial intelligence (AI) techniques in the industrial domain has shown promising results. These methodologies enable the analysis of images captured during the production process and the extraction of valuable information for monitoring and control purposes. By utilizing deep learning algorithms such as convolutional neural networks (CNN), it becomes possible to identify patterns, detect defects or anomalies, and provide instant feedback on the process’s performance. The scientific literature highlighted several successful cases of applying computer vision and AI-based monitoring systems [1, 2, 3, 4].
In this work, we focus on the development of a computer vision and AI-based monitoring system to replace the manual visual inspection of a specific stage in a production chain. The goal is to leverage the potential of computer vision techniques so as to identify process irregularities in real-time. Specifically, we design a deep network model able to detect the presence of an anticoagulant substance inside transparent tubes. We use real images acquired through a camera to train our model for the ability to distinguish between presence and absence of the reagent. This approach aims to optimize resource utilization, increase operational efficiency, and reduce waste in in dustrial processes, in order to: (i) align with the principles of sustainable manufacturing and (ii) contribute to the achievement of environmental and economic goals. Moreover, it offers several advantages, including the ability to monitor processes without the need for expensive dedicated sensors and the capability of identifying hidden problems that may escape other monitoring methods. Additionally, the use of images provides an intuitive visualization of the process, facilitating the understanding and enabling prompt interventions when necessary.
2. Method and results
As we stated in Section 1, this work addresses a specific industrial application, i.e. the detection of the presence of an anticoagulant substance inside vials within a production chain involved in manufacturing plastic consumables for analytical laboratories. To this end, we used a Deep Network architecture constituted by two main blocks:
• a 3-layer CNN neural network extracting relevant features;
• a 4-layer fully-connected network that performs the classification.
The model parameters have been chosen from scratch through an empirical process. The values of parameters
of our deep network model are provided in Table 1.
Our model has been trained by using images of the vials acquired through a camera situated on the top of the pipeline. We collected images of resolution 400 × 400 pixels. An example of the images is shown in Figure 1. Specifically, we acquired 402 images split into a training set, which contains 341 images, and a test set including 61 images. In both sets, half of the images refer to tubes containing the anticoagulant substance, while the other half concerns empty tubes. The CNN block takes images as inputs and extracts features as outputs, which in turn will be used as inputs of the classification block. The first layer has a number of input channels corresponding to the basic colors (i.e., red, green and blue). For the other layers, the number of input channels is provided in Eq. 1:
numin_channels(l) = numout_channels(l − 1), l > 1 (1)
where l indicates the layer id (see Table 1, first column).
The experiment has been replicated 10 times. Training lasted 20 epochs. We used the Adam optimizer [5] with weight decay. The learning rate has been set to 10−4 and the batch size to 16. With these settings, we achieved an average accuracy score of 100% over 10 replications of the experiment. This implies that all the trained models are able to correctly detect the presence/absence of the anticoagulant substance in the tube. The training and test errors are shown in Figure 2, left. By analyzing the detection ability of the best model, we can see that the confusion matrix (Figure 2, center) has no values outside the diagonal, i.e. no classification errors are performed. Furthermore, the ROC (Receiver Operating Characteristics) curve (Figure 2, right) corresponds to the ideal situation in which the classifier is able to distinguish between the positive class (presence of the anticoagulant) and the negative class (absence of the anticoagulant). Finally, the AUC (Area Under the Curve) score is equal to 1.0, thus indicating a perfect classifier.
3. Conclusions
In this work, we describe an automated system able to correctly detect the presence/absence of an anticoagulant substance in vials. The model has been trained on a small dataset collected in a company dealing with plastic consumables. Preliminary results show that the approach is promising, as the system successfully classifies all images in the dataset. Nonetheless, real industrial applications deal with large amount of data. Future work should be devoted to validate this approach on a wider dataset. In addition, future research directions may focus on refining and optimizing the proposed computer vision and AI-based monitoring system, exploring its applicability
in different industrial sectors, and investigating potential integration with other emerging technologies such as Internet of Things (IoT) and cloud computing for enhanced data analysis and decision-making processes.
REFERENCES
1. Agarwal, P., Aghaee, M., Tamer, M. and Budman, H. A novel unsupervised approach for batch process moni toring using deep learning, Computers & Chemical Engineering, 159, 107694, (2022).
2. Lyu, Y., Chen, J. and Song, Z. Image-based process monitoring using deep learning framework, Chemometrics
and Intelligent Laboratory Systems, 189, 8–17, (2019).
3. Wu, H. and Zhao, J. Self-adaptive deep learning for multimode process monitoring, Computers & chemical
engineering, 141, 107024, (2020).
4. Yuan, J. and Tian, Y. A multiscale feature learning scheme based on deep learning for industrial process
monitoring and fault diagnosis, IEEE Access, 7, 151189–151202, (2019).
5. Kingma, D. P. and Ba, J. Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, (2014)
Enhancing industrial quality control efficiency: an innovative deep learning approach for sustainable process monitoring
No full textA major concern in traditional industrial monitoring is the strong environmental impact,
mainly related to inefficiency of classic paradigms. In fact, typically monitoring systems
rely on the presence of human operators responsible for the detection of errors or faults.
However, this activity is heavily influenced by many factors like subjectivity or physical
conditions (e.g., fatigue, lighting), making this strategy ineffective in terms of costs (both
environmental and company-wide) and results. For instance, when the process involves
the control of production lots, if the operator identifies any anomalies the whole batch
is discarded. Sustainability and performance can be achieved by the automation of the
monitoring process. In this regard, we propose an innovative method based on a deep
neural network that can discriminate between correct and faulty items in a production
batch. Our model allows to significantly reduce disposal costs, since it analyzes each item
rather than considering the whole batch, thus preventing the waste of potentially usable
resources. Furthermore, the methodology enables the optimization of the monitoring
quality and lightens the responsibilities of the human operator, who only reviews the
model outputs and generates relevant statistics for the company. We provide a thorough
description of the proposed model in the context of the monitoring of transparent tubes
within the production process of a company dealing with plastic consumables. Preliminary
experiments we have performed on a real dataset confirm the effectiveness of the proposed
method
A computer vision-based quality assessment technique for the automatic control of consumables for analytical laboratories
No full textThe rapid growth of the Industry 4.0 paradigm is increasing the pressure to
develop effective automated monitoring systems. Artificial Intelligence (AI)
is a convenient tool to improve the efficiency of industrial processes while reducing
errors and waste. In fact, it allows the use of real-time data to increase
the effectiveness of monitoring systems, minimize errors, make the production
process more sustainable, and save costs. In this paper, a novel automatic
monitoring system is proposed in the context of production process of plastic
consumables used in analysis laboratories, with the aim to increase the effectiveness
of the control process currently performed by a human operator. In
particular, we considered the problem of classifying the presence or absence
of a transparent anticoagulant substance inside test tubes. Specifically, a
hand-designed deep network model is used and compared with some stateof-
the-art models for its ability to categorize different images of vials that can
be either filled with the anticoagulant or empty. Collected results indicate
that the proposed approach is competitive with state-of-the-art models in
terms of accuracy. Furthermore, we increased the complexity of the task by
training the models on the ability to discriminate not only the presence or
absence of the anticoagulant inside the vial, but also the size of the test tube.
The analysis performed in the latter scenario confirms the competitiveness
of our approach. Moreover, our model is remarkably superior in terms of
its generalization ability and requires significantly fewer resources. These
results suggest the possibility of successfully implementing such a model in
the production process of a plastic consumables company
Longstanding Eccrine Syringofibroadenoma With Evidence of Carcinomatous Transformation
No full textEccrine syringofibroadenoma (ESFA) is a cutaneous proliferation of eccrine ducts, mainly encountered in association with other dermatoses or skin tumors. Although a benign condition, either considered as a hamartoma or a reactive hyperplasia rather than a real neoplasm, some evidence suggests that longstanding ESFA can undergo malignant change. The recognition of such opportunity could have important therapeutic implications. We present a case of ESFA showing areas of carcinomatous transformation, discussing its morphological and immunohistochemical findings
Advanced computer Vision techniques for drug abuse detection
No full textLateral flow tests, used to rapidly detect various diseases, as HIV, or specific physiological conditions, as drug abuse, through blood, saliva, or urine samples, are becoming a powerful and cost-effective diagnostic tool. One major factor affecting the test result is the subjectivity of the operator's reading, which relies on both the interpretation of the results and the assessment of sample compliance. To overcome this issue, Computer Vision (CV) provides tools to mitigate the subjectivity of the results. Indeed, through sophisticated CV algorithms, it is possible to calibrate and normalize the result interpretation, taking into account individual variations [1] and environmental influences.
In this talk, we present an automated lateral flow test reader for drug abuse detection, enabling both operator-independent interpretation of results and objective validation of sample compliance through CV techniques. One of the main challenges addressed in this study is to tackle the issue of non-uniform lighting in the analysis scene, while at the same time dealing with the variability in the positioning of the regions of interest. We propose an innovative method for objectively detecting the presence or absence of illicit substances, establishing a threshold for positivity and assessing the suitability of the analyzed sample, regardless of the limitations and subjectivity associated with the operator. A combination of filtering, image enhancement, and segmentation techniques were employed to extract relevant information. Additionally, color balancing and clustering methods were used to investigate the colors of sample suitability indicators. The results demonstrate the effectiveness of the proposed method in improving objectivity in rapid lateral flow test results
An explainable convolutional neural network for the detection of drug abuse
No full textThe spread of Artificial Intelligence methods in many contexts is undeniable. Different models have been
proposed and applied to real-world applications in sectors like economy, industry, medicine, healthcare
and sports. Nevertheless, the reasons of why such techniques work are not investigated in depth, thus
posing questions about explainability, transparency and trust. In this work, we introduce a novel Deep
Learning approach for the problem of drug abuse detection. Specifically, we design a Convolutional
Neural Network model analyzing lateral-flow tests and discriminating between normal and abnormal
assays. Moreover, we provide evidence regarding the attributes that enable our model to address the
considered task, aiming to identify which parts of the input exert a significant influence on the network’s
output. This understanding is crucial for applying our methodology in real-world scenarios. The results
obtained demonstrate the validity of our approach. In particular, the proposed model achieves an excellent
accuracy in the classification of the lateral-flow tests and outperforms two state-of-the-art deep networks.
Additionally, we provide supporting data for the model’s explainability, ensuring a precise understanding
of the relationship between attributes and output, a key factor in comprehending the internal workings
of the neural network
Inflammatory pseudotumor-like follicular/fibroblastic dendritic cell sarcoma: focus on immunohistochemical profile and association with Epstein-Barr virus
No full textInflammatory pseudotumour-like follicular/fibroblastic dendritic cell sarcoma (IPT-like FDCS) is a rare EBV-associated variant of follicular dendritic cell sarcoma, usually arising in the liver or spleen and characterized by a favourable prognosis. The neoplastic cells show variable follicular dendritic cell or fibroblastic reticular cell differentiation and their immunoprofile is still poorly characterized. We describe a case of splenic IPT-like FDCS with unexpected CD31 expression and provide a concise review of English literature on the topic
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