Recurrent Convolutional Neural Network Model Research Articles

A Deep Learning Model with Axial Attention for Radar Echo Extrapolation

ABSTRACT Radar echo extrapolation is an important approach in precipitation nowcasting which utilizes historical radar echo images to predict future echo images. In this paper, we introduce the self-attention mechanism into Trajectory Gated Recurrent Unit (TrajGRU) model. Under the sequence-to-sequence framework, we have developed a novel convolutional recurrent neural network called Self-attention Trajectory Gated Recurrent Unit (SA-TrajGRU), which incorporates the self-attention mechanism. The SA-TrajGRU model which combines spatiotemporal variant structure in TrajGRU and self-attention module is simple and effective. We evaluate our approach on the Moving MNIST-2 dataset and the CIKM AnalytiCup 2017 radar echo dataset. The experimental results show that the performance of the proposed SA-TrajGRU model is comparable to other convolutional recurrent neural network models. HSS and CSI scores of the SA-TrajGRU model are higher than scores of other models under the radar echo threshold of 25 dBZ, indicating that the SA-TrajGRU model has the most accurate prediction results under this threshold.

Memorability shapes perceived time (and vice versa).

Visual stimuli are known to vary in their perceived duration. Some visual stimuli are also known to linger for longer in memory. Yet, whether these two features of visual processing are linked is unknown. Despite early assumptions that time is an extracted or higher-order feature of perception, more recent work over the past two decades has demonstrated that timing may be instantiated within sensory modality circuits. A primary location for many of these studies is the visual system, where duration-sensitive responses have been demonstrated. Furthermore, visual stimulus features have been observed to shift perceived duration. These findings suggest that visual circuits mediate or construct perceived time. Here we present evidence across a series of experiments that perceived time is affected by the image properties of scene size, clutter and memorability. More specifically, we observe that scene size and memorability dilate time, whereas clutter contracts it. Furthermore, the durations of more memorable images are also perceived more precisely. Conversely, the longer the perceived duration of an image, the more memorable it is. To explain these findings, we applied a recurrent convolutional neural network model of the ventral visual system, in which images are progressively processed over time. We find that more memorable images are processed faster, and that this increase in processing speed predicts both the lengthening and the increased precision of perceived durations. These findings provide evidence for a link between image features, time perception and memory that can be further explored with models of visual processing.

Information Extraction from Product Labels: A Machine Vision Approach

This research tackles the challenge of manual data extraction from product labels by employing a blend of computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition (OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food Facts API (Application Programming Interface) for database population and text-only label prediction. The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set. Importantly, our approach enables visually impaired individuals to access essential information on product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep learning and OCR in automating label extraction and recognition.

Noise-robust pipe wall-thinning discrimination system using convolution recurrent neural network model

Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression.

Unlocking the potential: CRNN-driven flexible mechanical sensors for intelligent sensing and beyond

In our rapidly evolving society, intelligent technology has become a driving force across various sectors, leading to transformative progress. This paper explores the integration of Convolutional Recurrent Neural Network (CRNN) algorithms with flexible sensors, showcasing their remarkable potential in enhancing data processing and interpretation. The synergy between CRNN and flexible sensors holds great promise in diverse domains, including health monitoring and industrial automation. Our comprehensive evaluation of the CRNN model reveals exceptional performance in classification and regression tasks, highlighting its adaptability to complex and varied sensor data. Additionally, confusion matrix analysis provides insights into multi-class classification scenarios, reinforcing the model's reliability. A comparative analysis against traditional machine learning techniques demonstrates the superiority of CRNN in handling time-series data. In conclusion, the integration of CRNN with flexible sensors is set to revolutionize intelligent sensing technology, opening new avenues for innovation and problem-solving in various industries. This collaborative approach not only enhances data accuracy and reliability but also paves the way for groundbreaking technologies and applications yet to be imagined. The future of intelligent sensing is here, and it is both exciting and promising, with CRNN-driven flexible sensors leading the way towards a more intelligent and connected world.

Learning-based surface deformation recovery for large radio telescope antennas

Abstract The surface deformation of the main reflector in a large radio telescope is closely related to its working efficiency, which is important for some astronomical science studies. Here, we present a deep learning-based surface deformation recovery framework using non-interferometric intensity measurements as input. The recurrent convolutional neural network (RCNN) is developed to establish the inverse mapping relationship between the surface deformation of the main reflector and the intensity images at the aperture plane and at a near-field plane. Meanwhile, a physical forward propagation model is adopted to generate a large amount of data for pre-training in a computationally efficient manner. Then, the inverse mapping relationship is adjusted and improved by transfer learning using experimental data, which achieves a 15-fold reduction in the number of training image sets required, which is helpful to facilitate the practical application of deep learning in this field. In addition, the RCNN model can be trained as a denoiser, and it is robust to the axial positioning error of the measuring points. It is also promising to extend this method to the study of adaptive optics.

Attention-based 3D convolutional recurrent neural network model for multimodal emotion recognition.

Multimodal emotion recognition has become a hot topic in human-computer interaction and intelligent healthcare fields. However, combining information from different human different modalities for emotion computation is still challenging. In this paper, we propose a three-dimensional convolutional recurrent neural network model (referred to as 3FACRNN network) based on multimodal fusion and attention mechanism. The 3FACRNN network model consists of a visual network and an EEG network. The visual network is composed of a cascaded convolutional neural network-time convolutional network (CNN-TCN). In the EEG network, the 3D feature building module was added to integrate band information, spatial information and temporal information of the EEG signal, and the band attention and self-attention modules were added to the convolutional recurrent neural network (CRNN). The former explores the effect of different frequency bands on network recognition performance, while the latter is to obtain the intrinsic similarity of different EEG samples. To investigate the effect of different frequency bands on the experiment, we obtained the average attention mask for all subjects in different frequency bands. The distribution of the attention masks across the different frequency bands suggests that signals more relevant to human emotions may be active in the high frequency bands γ (31-50 Hz). Finally, we try to use the multi-task loss function Lc to force the approximation of the intermediate feature vectors of the visual and EEG modalities, with the aim of using the knowledge of the visual modalities to improve the performance of the EEG network model. The mean recognition accuracy and standard deviation of the proposed method on the two multimodal sentiment datasets DEAP and MAHNOB-HCI (arousal, valence) were 96.75 ± 1.75, 96.86 ± 1.33; 97.55 ± 1.51, 98.37 ± 1.07, better than those of the state-of-the-art multimodal recognition approaches. The experimental results show that starting from the multimodal information, the facial video frames and electroencephalogram (EEG) signals of the subjects are used as inputs to the emotion recognition network, which can enhance the stability of the emotion network and improve the recognition accuracy of the emotion network. In addition, in future work, we will try to utilize sparse matrix methods and deep convolutional networks to improve the performance of multimodal emotion networks.

Exploring the Online Platform Construction of English Movie Audiovisual Speaking Blended Teaching Mode Based on POA Theory of Production-Oriented Approach

Abstract This article is concerned with improving college students’ English audiovisual speaking ability in the context of the development of the times. Based on the SPOC (Small Private Online Course) teaching platform and the POA (Production-Oriented Approach) theory, the article constructs a blended teaching mode of English movie audiovisual speaking course. This model extracts English movie audiovisual features through the multimodal fusion of audiovisual correlation perception and audiovisual recognition. The article adopts CRNN (Convolutional Recurrent Neural Network) model and 3D convolutional structure to build a multimodal audiovisual speech recognition model for English movies. The differences between blended and traditional teaching are analyzed and the effects of integrated teaching are explored through a study of South China Agricultural University students. The study results show that the combined teaching is 0.699 points higher than the traditional teaching method in the average normalized gain score, and the audiovisual speaking ability of the students in the experimental class has improved by 76.04%. The blended teaching of English movie audiovisual speaking based on SPOC platform can effectively enhance students’ audiovisual speaking ability, thus promoting students’ cross-cultural communication ability.

License plate recognition system in unconstrained scenes via a new image correction scheme and improved CRNN

Automatic license plate recognition plays an important role in vehicle management. The performance of current license plate recognition systems is significantly affected by large-angle deflection in unconstrained scenes. For addressing this issue, this paper develops a license plate recognition system via a new image correction scheme and an improved CRNN (Convolutional Recurrent Neural Network). In which, the yolov5l is firstly introduced for license plate detection, and it is trained by transfer learning in order to address insufficient data. Then, a new license plate correction scheme and AFF-Net (Adaptive Fusion Feature Segmentation Network) is proposed and applied, which uses the segmentation result of license plate and its original image area for perspective transformation to improve the correction effect. Finally, the channel attention mechanism is added to the CRNN model for license plate recognition, so that a single grid cell of the feature network can obtain more spatial information. The recognition accuracy of the developed license plate recognition system is 98.8 % on CTPFSD (China Temporary Parking Fee System Data), which is much higher than current mainstream license plate recognition systems. More importantly, the developed license plate recognition system has been deployed in the cloud and applied in outdoor parking toll in practice.

A CRNN‐based method for Chinese ship license plate recognition

AbstractExisting deep learning methods cannot achieve satisfactory ship license plate (SLP) recognition due to the harsh marine environment, such as foggy weather, unstable ship state and small targets. Therefore, a convolutional recurrent neural network (CRNN)‐based method is proposed for accurate SLP image recognition. Overall, the suggested method improves a CRNN recognition model by SLP image enhancement and data augmentation. The SLP image enhancement employs dark channel prior and Hough transform line detector to address the fog/blurriness and tilt issues existing in SLP images. As separate and joint operations, the two enhancements contribute to data augmentation for CRNN recognition. Preprocessing algorithms, including adaptive histogram equalization and image edge padding, are used to improve and unify the enlarged dataset for augmenting the CRNN model. As a final step, correction of the CRNN recognition results is made according to the character rule of SLPs, using an edit‐distance algorithm to match against a pre‐established SLP dictionary. A variety of real SLP images were collected to build an SLP image dataset for verification. The experimental results indicate that our method can reach an SLP recognition accuracy of , which is significantly superior to other text‐based deep learning methods.

Text Recognition Model Based on Multi-Scale Fusion CRNN.

Scene text recognition is a crucial area of research in computer vision. However, current mainstream scene text recognition models suffer from incomplete feature extraction due to the small downsampling scale used to extract features and obtain more features. This limitation hampers their ability to extract complete features of each character in the image, resulting in lower accuracy in the text recognition process. To address this issue, a novel text recognition model based on multi-scale fusion and the convolutional recurrent neural network (CRNN) has been proposed in this paper. The proposed model has a convolutional layer, a feature fusion layer, a recurrent layer, and a transcription layer. The convolutional layer uses two scales of feature extraction, which enables it to derive two distinct outputs for the input text image. The feature fusion layer fuses the different scales of features and forms a new feature. The recurrent layer learns contextual features from the input sequence of features. The transcription layer outputs the final result. The proposed model not only expands the recognition field but also learns more image features at different scales; thus, it extracts a more complete set of features and achieving better recognition of text. The results of experiments are then presented to demonstrate that the proposed model outperforms the CRNN model on text datasets, such as Street View Text, IIIT-5K, ICDAR2003, and ICDAR2013 scenes, in terms of text recognition accuracy.

Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

Accurately picking P- and S-wave arrivals of microseismic (MS) signals in real-time directly influences the early warning of rock mass failure. A common contradiction between accuracy and computation exists in the current arrival picking methods. Thus, a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network (CRNN). This method fully utilizes the advantages of convolutional layers and gated recurrent units (GRU) in extracting short- and long-term features, in order to create a precise and lightweight arrival picking structure. Then, the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model. The actual operation on various devices indicates that compared with the U-Net method, the CRNN method achieves faster arrival picking with less performance consumption. An application of large underground caverns in the Yebatan hydropower station (YBT) project shows that compared with the short-term average/long-term average (STA/LTA), Akaike information criterion (AIC) and U-Net methods, the CRNN method has the highest accuracy within four sampling points, which is 87.44% for P-wave and 91.29% for S-wave, respectively. The sum of mean absolute errors (MAESUM) of the CRNN method is 4.22 sampling points, which is lower than that of the other methods. Among the four methods, the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure, which occurs at the junction of the shaft and the second gallery. Thus, the proposed method can pick up P- and S-arrival accurately and rapidly, providing a reference for rock failure analysis and evaluation in engineering applications.

English

The latest wireless sensor network (WSN) developments in critical applications have introduced security risks, like jamming. Intrusion Detection System (IDS) in WSN is the method of recognizing malevolent or unauthorized activities in the network. The intruder's presence to launch different attacks within the network cannot be disregarded. Despite a great deal of effort by the researcher workers, IDS still experienced difficulties enhancing recognition performance while minimizing the false alarm rate and identifying novel intrusions. Recently, Deep Learning (DL) and Machine Learning (ML) based IDS system has been deployed as promising solution to effectively identify intrusion across the network. Therefore, the study presents a Moth Search Optimization with DL-based Intrusion Detection (MSODL-ID) method in the WSN. The MSODL-ID technique aims to effectually identify the occurrence of malicious activities or intrusions in the network. To accomplish this, the MSODL-ID technique undergoes two stages of preprocessing: data conversion and data scaling. In addition, the MSODL-ID technique employs Convolutional Recurrent Neural Network (CRNN) model with a Hopfield layer for intrusion detection purposes. For optimal hyperparameter selection of the CRNN model, the MSO algorithm is used and thereby enhances the classification performance of the CRNN model. The stimulation analysis of the MSODL-ID system is tested by means of Kaggle datasets, and the outcomes exhibit the promising performance of the MSODL-ID system over other current DL approaches.

A Convolutional Recurrent Neural-Network-Based Machine Learning for Scene Text Recognition Application

Optical character recognition (OCR) is the process of acquiring text and layout information through analysis and recognition of text data image files. It is also a process to identify the geometric location and orientation of the texts and their symmetrical behavior. It usually consists of two steps: text detection and text recognition. Scene text recognition is a subfield of OCR that focuses on processing text in natural scenes, such as streets, billboards, license plates, etc. Unlike traditional document category photographs, it is a challenging task to use computer technology to locate and read text information in natural scenes. Imaging sequence recognition is a longstanding subject of research in the field of computer vision. Great progress has been made in this field; however, most models struggled to recognize text in images of complex scenes with high accuracy. This paper proposes a new pattern of text recognition based on the convolutional recurrent neural network (CRNN) as a solution to address this issue. It combines real-time scene text detection with differentiable binarization (DBNet) for text detection and segmentation, text direction classifier, and the Retinex algorithm for image enhancement. To evaluate the effectiveness of the proposed method, we performed experimental analysis of the proposed algorithm, and carried out simulation on complex scene image data based on existing literature data and also on several real datasets designed for a variety of nonstationary environments. Experimental results demonstrated that our proposed model performed better than the baseline methods on three benchmark datasets and achieved on-par performance with other approaches on existing datasets. This model can solve the problem that CRNN cannot identify text in complex and multi-oriented text scenes. Furthermore, it outperforms the original CRNN model with higher accuracy across a wider variety of application scenarios.

Spatiotemporal Prediction Method for Vorticity of Three-dimensional Near-wall Cylindrical Flow Based on Convolutional Recurrent Neural Network

Due to the existence of a wall surface, the flow around the near-wall cylinder is a very complex unsteady dynamic behavior. However, this kind of feature extraction and fast prediction method of complex flow is difficult to solve in engineering. Therefore, a data-driven model based on Convolutional Recurrent Neural Network (CRNN) is proposed to solve the feature extraction and fast prediction of complex flow, and to predict the vorticity of the near wake region of a three-dimensional near-wall cylinder with G/D=1. CRNN model can analyze the relationship between Spatiotemporal evolution. The three-dimensional flow around the near-wall cylinder with Reynolds number 1500 meshed, and the vorticity was calculated after grid calculation to establish the Spatiotemporal evolution data set. For machine learning problems with high dimensions and large amounts of data, the Adaptive Moment (Adam) estimation method served as an effectively optimize. According to the Spatiotemporal prediction results of vorticity, the prediction results of the model fit well with the numerical simulation results, and the vorticity of the future time step is successfully predicted on the space and time series, that is, the evolutionary relationship of the Spatiotemporal series of vorticity near the wake of the near-wall cylinder is established. After the above model is established, only the initial multi-time vorticity data need to be obtained. The model can serve as predicting the vorticity field in the near wake region of the near-wall cylinder in the future.

Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices

The ongoing reading process of digital meters is time-consuming and prone to errors, as operators capture images and manually update the system with the new readings. This work proposes to automate this operation through a deep learning-powered solution for universal controllers and flow meters that can be seamlessly incorporated into operators’ existing workflow. Firstly, the digital display area of the equipment is extracted with a screen detection module, and a perspective correction step is performed. Subsequently, the text regions are identified with a fine-tuned EAST text detector, and the important readings are selected through template matching based on the expected graphical structure. Finally, a fine-tuned convolutional recurrent neural network model recognizes the text and registers it. Evaluation experiments confirm the robustness and potential for workload reduction of the proposed system, which correctly extracts 55.47% and 63.70% of the values for reading in universal controllers, and 73.08% of the values from flow meters. Furthermore, this pipeline performs in real time in a low-end mobile device, with an average execution time in preview of under 250 ms for screen detection and on an acquired photo of 1500 ms for the entire pipeline.

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Brain disorders are a significant source of economic strain and unfathomable suffering in modern society. Imaging techniques help diagnose, monitor and treat mental health, neurological, and developmental disorders. To aid in the Computer-Aided Diagnosis of brain diseases, deep learning (DL) was used for the analysis of neuroimages from modalities including Positron Emission Tomography (PET), Structural Magnetic Resonance Imaging (SMRI), and functional MRI. In this study, a Whale Optimization Algorithm is used with Deep Learning to analyse MRI scans for signs of neurological disease. WOADL-BDDC may detect and label abnormalities in the brain based on an MRI scan. It uses a two-step pre-processing procedure, first using guided filtering to get rid of background noise and then using U-Net segmentation to get rid of the top of the head. QuickNAT, along with RMSProp, is used to segment the brain. When analysing data, WOADL-BDDC uses radionics to collect information from every layer. When used in a convolutional recurrent neural network model, the Whale Optimization Algorithm may accurately categorize mental illness. WOADL-BDDC is put through its paces using ADNI 3D. Compared to state-of-the-art classification results from Vgg16, Graph CNN, Modified ResNet18, Non-linear SVM, ResNet50-SVM, ResNet50-RF, the suggested technique achieved the greatest accuracy. It was demonstrated that the suggested model is superior to other models for classification from MRI images. In simulations, the proposed approach is shown to be effective in optimizing hyperparameters with an accuracy of 94.38 % on TR set and 94.87% on TS set, Precision of 96.43% on TR set and 97.62% on TS set, and an F1-Score of 89.35 % and 92.10% on TR and TS set, respectively.

Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network

Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network (CRN) that addresses the speech enhancement in a real-time single channel or track of sound created by the speaker. In the first stage mask based long short-term memory (LSTM) is used for noise suppression along with loss function and in the second stage, Convolutional Encoder-Decoder (CED) is used for speech restoration. The proposed model is evaluated on various speaker and noisy environments like Babble noise, car noise, and white Gaussian noise. The proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for training. The performance of the proposed model is outstanding even in low Signal to Noise Ratio (SNR).

Modeling of Combustion Characteristics of Particles in Transient Gas–Solid Reacting Flow via a Machine Learning Approach

Particle group combustion presents a strong temporal and spatial inhomogeneity owing to the complicated interphase interactions. Based on the data set from the fictitious domain method, the recurrent fully connected and convolutional parallel neural network (R-FC&CNN) architecture and its two comparable simplified models, that is, the recurrent fully connected neural network (R-FCNN) and the recurrent convolutional neural network (R-CNN) architectures, were constructed for predicting the gas–solid momentum exchange coefficient, β (kg·s–1·m–3), average combustion rate per unit surface area of particles, rc¯/A (kg·s–1·m–2), and comprehensive NaCl release parameter, γ, selectively. A time sequence of average particle temperature, T̅ (K), and particle volume fraction, ε, which can be extended in the matrix form, were constructed as the features selectively according to their correlation with the target physical quantity. The average relative error, δ̅, and coefficient of determination, R2, were used as the evaluators. Through final testing, in the mild combustion domain, the R-CNN and R-FCNN models with simple structures showed good performance for β(δ̅ = 0.13, R2 = 0.8) and rc¯/A (δ̅ = 0.04, R2 = 0.84), respectively, while in the severe combustion domain, the R-FC&CNN model, with more complete features and functional structure, performed the best (for β, δ̅ = 0.12, R2 = 0.85; for rc¯/A, δ̅ = 0.05, R2 = 0.92; and for γ, δ¯=0.05,R2=0.96). A fine-tuning and interpolated prediction method was developed to investigate further the model’s expansibility. Both ensured acceptable performance on a new similar problem. In summary, the feasibility of physical meaning-oriented machine learning--based model(s) for predicting the combustion characteristics of nonuniformly distributed particles was confirmed.

Deep Learning-Based BoVW–CRNN Model for Lung Tumor Detection in Nano-Segmented CT Images

One of the most common oncologies analyzed among people worldwide is lung malignancy. Early detection of lung malignancy helps find a suitable treatment for saving human lives. Due to its high resolution, greater transparency, and low noise and distortions, Computed Tomography (CT) images are most commonly used for processing. In this context, this research work mainly focused on the multifaceted nature of lung cancer diagnosis, a quintessential, fascinating, and risky subject of oncology. The input used here has been nano-image, enhanced with a Gabor filter and modified color-based histogram equalization. Then, the image of lung cancer was segmented by using the Guaranteed Convergence Particle Swarm Optimization (GCPSO) algorithm. A graphical user interface nano-measuring tool was designed to classify the tumor region. The Bag of Visual Words (BoVW) and a Convolutional Recurrent Neural Network (CRNN) were employed for image classification and feature extraction processes. In terms of findings, we achieved the average precision of 96.5%, accuracy of 99.35%, sensitivity of 97%, specificity of 99% and F1 score of 95.5%. With the proposed solution, the overall time required for the segmentation of images was much smaller than the existing solutions. It is also remarkable that biocompatible-based nanotechnology was developed to distinguish the malignancy region on a nanometer scale and has to be evaluated automatically. That novel method succeeds in producing a proficient, robust, and precise segmentation of lesions in nano-CT images.