VGG16 Network Research Articles

Neural Network Classification Algorithm Based on Self-attention Mechanism and Ensemble Learning for MASLD Ultrasound Images

BackgroundUltrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) due to its non-invasive nature. Computer-aided diagnosis (CAD) technology can assist doctors in avoiding deviations in the detection and classification of MASLD. MethodWe propose a hybrid model that integrates the pre-trained VGG16 network with an attention mechanism and a stacking ensemble learning model, which is capable of multi-scale feature aggregation based on the self-attention mechanism and multi-classification model fusion (Logistic regression, random forest, support vector machine) based on stacking ensemble learning. The proposed hybrid method achieves four classifications of normal, mild, moderate, and severe fatty liver based on ultrasound images. Result and conclusionOur proposed hybrid model reaches an accuracy of 91.34% and exhibits superior robustness against interference, which is better than traditional neural network algorithms. Experimental results show that, compared with the pre-trained VGG16 model, adding the self-attention mechanism improves the accuracy by 3.02%. Using the stacking ensemble learning model as a classifier further increases the accuracy to 91.34%, exceeding any single classifier such as LR (89.86%) and SVM (90.34%) and RF (90.73%). The proposed hybrid method can effectively improve the efficiency and accuracy of MASLD ultrasound image detection.

Research on Internal Damage Identification of Wire Rope Based on Improved VGG Network.

In order to solve the problem of great difficulty in detecting the internal damage of wire rope, this paper proposes a method to improve the VGG model to identify the internal damage of wire rope. The short-time Fourier transform method is used to transform the wire rope damage signal into a time-frequency spectrogram as the model input, and then the traditional VGG model is improved from three aspects: firstly, the attention mechanism module is introduced to increase the effective feature weights, which effectively improves the recognition accuracy; and then, the batch normalization layer is added to carry out a uniform normalization of the data, so as to make the model easier to converge. At the same time, the pooling layer and the fully connected layer are improved to solve the redundancy problem of the traditional VGG network model, which makes the model structure more lightweight, greatly saves the computational cost, shortens the training time, and finally adopts the joint-sample uniformly distributed cross-entropy as the loss function to solve the overfitting problem and further improve the recognition rate. The experimental results show that the improved VGG model has an identification accuracy of up to 98.84% for the internal damage spectrogram of the wire rope, which shows a good identification ability. Not only that, but the model is also superior, with less time-consuming training and stronger generalization ability.

Classification of Marine Fish Based on Image Using Convolutional Neural Network Algorithm and VGG16

Marine fish are one of the many natural resources that frequently consumed by people. However, several types of marine fish are prohibited from consumption due to being nearly extinct. Additionally, some fish species contain high levels of mercury that can be harmful to human if consumed. Because of the large number of marine fish species, it becomes challenging to identify them without knowledge of fisheries. Moreover, computers have become highly advanced devices that facilitate various human activities. This advancement allows for the creation of systems capable of processing information from images, known as image classification. There are numerous methods that can be employed in designing an image classification system, one of which is transfer learning. This study was aimed to design an image classification system using the transfer learning method with a pre-trained VGG16 model and Convolutional Neural Network algorithm. The research results showed a training data accuracy of 100% and a validation data accuracy of 99.3%, with an overall accuracy of 84% and a loss value of 0.6591.

Optimizing image captioning: The effectiveness of vision transformers and VGG networks for remote sensing

This study presents a comprehensive evaluation of two prominent deep learning models, Vision Transformer (ViT) and VGG16, within the domain of image captioning for remote sensing data. By leveraging the BLEU score, a widely accepted metric for assessing the quality of text generated by machine learning models against a set of reference captions, this research aims to dissect and understand the capabilities and performance nuances of these models across various sample sizes: 25, 50, 75, and 100 samples. Our findings reveal that the Vision Transformer model generally outperforms the VGG16 model across all evaluated sample sizes, achieving its peak performance at 50 samples with a BLEU score of 0.5507. This performance shows that ViT benefits from its ability to capture global dependencies within the data, providing a more nuanced understanding of the images. However, the performance slightly decreases as the sample size increases beyond 50, indicating potential challenges in scalability or overfitting to the training data. Conversely, the VGG16 model shows a different performance trajectory, starting with a lower BLEU score for smaller sample sizes but demonstrating a consistent improvement as the sample size increases, culminating in its highest BLEU score of 0.4783 for 100 samples. This pattern suggests that VGG16 may require a larger dataset to adequately learn and generalize from the data, although it achieves a more modest performance ceiling compared to ViT. Through a detailed analysis of these findings, the study underscores the strengths and limitations of each model in the context of image captioning. The Vision Transformer's superior performance highlights its potential for applications requiring high accuracy in text generation from images. In contrast, the gradual improvement exhibited by VGG16 suggests its utility in scenarios where large datasets are available, and scalability is a priority. This study contributes to the ongoing discourse in the AI community regarding the selection and optimization of deep learning models for complex tasks such as image captioning, offering insights that could guide future research and application development in this field.

Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.

Diagnosis of COVID-19 CT Scans Using Convolutional Neural Networks

Machine learning technology, particularly neural networks, provides useful tools for diagnosing diseases. This study focuses on how convolutional neural networks can be implemented to diagnose COVID-19 through the processing of x-ray images. This study demonstrates how the convolutional neural networks DenseNet201, ResNet152, VGG16, and InceptionV3 can aid healthcare providers in the diagnosis of COVID-19. The models returned accuracies of 98.73%, 97.23%, 91.25% and 98.38% respectively. The results from these experiments are compared to previous studies by evaluating F1-score, accuracy, precision and recall. Additionally, the important problems of hyperparameter tuning and data imbalance are explored and addressed. Areas for future research in this area are also suggested.

Identification of the Structure of Liquid–Gas Flow in a Horizontal Pipeline Using the Gamma-Ray Absorption and a Convolutional Neural Network

Knowledge of the liquid–gas flow regime is important for the proper control of many industrial processes (e.g., in the mining, nuclear, petrochemical, and environmental industries). The latest publications in this field concern the use of computational intelligence methods for flow structure recognition, which include, for example, expert systems and artificial neural networks. Generally, machine learning methods exploit various characteristics of sensors signals in the value, time, frequency, and time–frequency domain. In this work, the convolutional neural network (CNN) VGG-16 is applied for analysis of histogram images of signals obtained for water–air flow by using gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with a radiometric measurement system. The essential part of the hydraulic installation is a horizontal pipeline made of metalplex, 4.5 m long, with an internal diameter of 30 mm. The radiometric measurement set used in the investigation consists of a linear Am-241 radiation source with an energy of 59.5 keV and a scintillation detector with a NaI(Tl) crystal. In this work, four types of water–air flow regimes (plug, slug, bubble, and transitional plug–bubble) were studied. MATLAB 2022a software was used to analyze the measurement signal obtained from the detector. It was found that the CNN network correctly recognizes the flow regime in more than 90% of the cases.

Intelligent bell facial paralysis assessment: a facial recognition model using improved SSD network

With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients’ facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell’s facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients’ condition. By replacing the VGG network with a more efficient backbone, we improved the model’s accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation.

High Performance CNN Accelerators based on Hardware and Algorithm Co-Optimization

The efficacy of convolutional neural networks (CNNs) has led to their extensive application in picture recognition and categorization. Nevertheless, embedded systems face challenges when it comes to storing the massive volumes of weight data required by CNNs in their on-chip memory. Pruning a convolutional neural network (CNN) model can reduce its size with no impact on accuracy; however, when used with a parallel architecture, the resulting model will be slower to execute. A CNN compression method that is hardware-centric is introduced in this paper. A model of a deep neural network (DNN) will include "pruning layers (P-layers)" and "no-pruning layers (NP-layers)" attached to it. For effective and parallel processing, an NP-layer employs a regular distribution for its weights. Pruning causes a P-layer to have an irregular form, yet the high compression ratio it yields makes the shape undesirable. Using uniform and gradual quantization methods, we can achieve a processing efficiency/compression ratio trade-off with a small loss of precision. Integrating multiple parallel finite impulse response (FIR) filters into an NP-layer distributed convolutional architecture, we further improve the regular model. The P-layer irregular sparse model is suggested to use an ADF-based processing element. The suggested compression strategy and hardware architecture can be utilised by a hardware/algorithm co-optimization (HACO) method to run an NP→P hybrid compressed CNN model on FPGAs. The VGG-16 network achieves a compression ratio of 27.5× with a top-5 accuracy loss of 0.44% by using a hardware accelerator on a single FPGA chip without off-chip memory. A result of 83.0 FPS, or 1.8 times quicker, was achieved when the compressed VGG-16 model was implemented on a Xilinx VCU118 evaluation board for image applications. Index Terms - CNN, Accelerators, Co-Optimization.

Lung Tumor Classification Optimizer with Augment Input Images

This work focuses on the accurate classification of lung tumors in Computed Tomography (CT) images to determine tumor type and stage, essential for guiding treatment decisions. In this research, pretrained model VGG19 and Generative Adversarial Networks (GANs) models are used to classify the lung tumor in order to enhance the classification accuracy by increasing the diversity of the training dataset. The dataset comprises 1097 CT image samples, with 120 classified as benign, 561 as malignant, and 416 as normal cases. The coding for this work was developed using Python, with TensorFlow as the deep learning platform, and simulations were conducted on Google Colab. Performance metrics such as accuracy, sensitivity, specificity, and F1 score are evaluated to assess the effectiveness of the classification model. The datasets were obtained from the Iraq-Oncology Teaching Hospital.

Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks.

Detection of bacterial spot disease on tomato leaves using a Convolutional Neural Network (CNN)

Tomato diseases have become a major concern to the tomato production sector around the world. A huge proportion of tomato crops deteriorate yearly during growth or after harvesting due to the infections caused by fungus, viruses and bacteria. Early detection of these diseases plays a crucial role in alleviating overall production loss. Over the past decades, farmers have been using visual observation to identify a crop disease in a field. However, the visual observation method is labour intensive, time consuming, and prone to human error. Currently, intelligent approaches have been widely used to detect and classify these diseases. The objective of this study is to design a convolutional neural network VGG16 net architecture that is able to detect tomato diseases on tomato leaves. A model that can successfully detect 5 tomato leaf diseases was developed: (1) Bacterial spot, (2) Early blight, (3) Late blight, (4) Septoria leaf and (5) Yellow leaf curl with a training accuracy of 0.9328.

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation.

Forests play a pivotal role in mitigating climate change as well as contributing to the socio-economic activities of many countries. Therefore, it is of paramount importance to monitor forest cover. Traditional machine learning classifiers for segmenting images lack the ability to extract features such as the spatial relationship between pixels and texture, resulting in subpar segmentation results when used alone. To address this limitation, this study proposed a novel hybrid approach that combines deep neural networks and machine learning algorithms to segment an aerial satellite image into forest and non-forest regions. Aerial satellite forest image features were first extracted by two deep neural network models, namely, VGG16 and ResNet50. The resulting features are subsequently used by five machine learning classifiers including Random Forest (RF), Linear Support Vector Machines (LSVM), k-nearest neighbor (kNN), Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB) to perform the final segmentation. The aerial satellite forest images were obtained from a deep globe challenge dataset. The performance of the proposed model was evaluated using metrics such as Accuracy, Jaccard score index, and Root Mean Square Error (RMSE). The experimental results revealed that the RF model achieved the best segmentation results with accuracy, Jaccard score, and RMSE of 94%, 0.913 and 0.245, respectively; followed by LSVM with accuracy, Jaccard score and RMSE of 89%, 0.876, 0.332, respectively. The LDA took the third position with accuracy, Jaccard score, and RMSE of 88%, 0.834, and 0.351, respectively, followed by GNB with accuracy, Jaccard score, and RMSE of 88%, 0.837, and 0.353, respectively. The kNN occupied the last position with accuracy, Jaccard score, and RMSE of 83%, 0.790, and 0.408, respectively. The experimental results also revealed that the proposed model has significantly improved the performance of the RF, LSVM, LDA, GNB and kNN models, compared to their performance when used to segment the images alone. Furthermore, the results showed that the proposed model outperformed other models from related studies, thereby, attesting its superior segmentation capability.

PLANT DISEASE RECOGNITION USING VGG-16

Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

Research on Input Schemes for Polarimetric SAR Classification Using Deep Learning

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations were designed, and the classification accuracy of various approaches was evaluated using the classic convolutional neural networks (CNNs) AlexNet and VGG16. The findings reveal that the commonly employed six-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete nine-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other seven schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

Comparative Analysis of CNN Architectures for SIBI Image Classification

The classification of images from the Indonesian Sign Language System (SIBI) using VGG16, ResNet50, Inception, Xception, and MobileNetV2 Convolutional Neural Network (CNN) architectures is evaluated in this paper. With Google Colab Pro, a 224 × 224-pixel picture dataset was used for the study. A five-stage technique consisting of Dataset Collection, Dataset Preprocessing, Model Design, Model Training, and Model Testing was applied. Performance evaluation focused on accuracy, precision, recall, and F1-Score. The results identified VGG16 as the top-performing model with an accuracy of 99.60% and an equivalent F1-Score, followed closely by ResNet50 with nearly similar performance. Inception, XCeption, and MobileNetV2 demonstrated balanced performance but with lower accuracy. This study sheds light on the best CNN models to choose for SIBI image classification, and it makes recommendations for further research that include using sophisticated data augmentation methods, investigating novel CNN architectures, and putting the models to practical use.

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

Age-Invariant Cross-Age Face Verification using Transfer Learning

The integration of face verification technology has become indispensable in numerous safety and security software systems. Despite its promising results, the field of face verification encounters significant challenges due to age-related disparities. Human facial characteristics undergo substantial transformations over time, leading to diverse variations including changes in facial texture, morphology, facial hair, and eyeglass adoption. This study presents a pioneering methodology for cross-age face verification, utilizing advanced deep learning techniques to extract resilient and distinctive facial features that are less susceptible to age-related fluctuations. The feature extraction process combines handcrafted features like Local Binary Pattern/Histogram of Oriented Gradients with deep features from MobileNetV2 and VGG-16 networks. As the texture of the facial skin defines the age related characteristic the well-known texture feature extractors like LBP and HoG is preferred. These features are concatenated to achieve fusion, and subsequent layers fine-tune them. Experimental validation utilizing the Cross-Age Celebrity Dataset demonstrates remarkable efficacy, achieving an accuracy of 98.32%.

Hybrid Artificial Intelligence Approach to COVID-19 Diagnosis from CT Images: Deep Networks and Classification Analysis

Using lung images obtained by computed tomography (CT), this study aims to detect coronavirus (Covid-19) disease with deep learning (DL) techniques. The study included 751 lung CT images from 118 Covid-19 patients and 628 lung CT images from 100 healthy individuals. In total, 70% of the 1379 images were used for training and 30% for testing. In the study, two different methods were proposed on the same dataset. In the first method, the images were trained on AlexNet, VGG-16, VGG-19, GoogleNet and a proposed network. The performance metrics obtained from the five networks were compared and it was observed that the proposed network achieved the highest accuracy value with 95.61%. In the second method, the images were trained on VGG-16, VGG-19, DenseNet-121, ResNet-50 and MobileNet networks. Among the image features obtained from each of these networks, the best 1000 features were selected by Principal Component Analysis (PCA). The best 1000 features were classified with Random Forest (RF) and Support Vector Machines (SVM). According to the classification results, the best 1000 features selected from the features extracted by the VGG-16 and MobileNet networks were obtained with the highest accuracy rate of 93.94% using SVM. It is thought that this study can be a helpful tool in the diagnosis of Covid-19 disease while reducing time and labor costs with the use of artificial intelligence (AI).

Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification.

Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.