Image Preprocessing Research Articles

RDAG U-Net: An Advanced AI Model for Efficient and Accurate CT Scan Analysis of SARS-CoV-2 Pneumonia Lesions.

Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new model called Residual-Dense-Attention Gates U-Net (RDAG U-Net) to improve accuracy and efficiency in identification. Methods: This study employed Attention U-Net, Attention Res U-Net, and the newly developed RDAG U-Net model. RDAG U-Net extends the U-Net architecture by incorporating ResBlock and DenseBlock modules in the encoder to retain training parameters and reduce computation time. The training dataset in-cludes 3,520 CT scans from an open database, augmented to 10,560 samples through data en-hancement techniques. The research also focused on optimizing convolutional architectures, image preprocessing, interpolation methods, data management, and extensive fine-tuning of training parameters and neural network modules. Result: The RDAG U-Net model achieved an outstanding accuracy of 93.29% in identifying pulmonary lesions, with a 45% reduction in computation time compared to other models. The study demonstrated that RDAG U-Net performed stably during training and exhibited good generalization capability by evaluating loss values, model-predicted lesion annotations, and validation-epoch curves. Furthermore, using ITK-Snap to convert 2D pre-dictions into 3D lung and lesion segmentation models, the results delineated lesion contours, en-hancing interpretability. Conclusion: The RDAG U-Net model showed significant improvements in accuracy and efficiency in the analysis of CT images for SARS-CoV-2 pneumonia, achieving a 93.29% recognition accuracy and reducing computation time by 45% compared to other models. These results indicate the potential of the RDAG U-Net model in clinical applications, as it can accelerate the detection of pulmonary lesions and effectively enhance diagnostic accuracy. Additionally, the 2D and 3D visualization results allow physicians to understand lesions' morphology and distribution better, strengthening decision support capabilities and providing valuable medical diagnosis and treatment planning tools.

Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.

Dysregulation of lung tissue collagen level plays a vital role in understanding how lung diseases progress. However, traditional scoring methods rely on manual histopathological examination introducing subjectivity and inconsistency into the assessment process. These methods are further hampered by inter-observer variability, lack of quantification, and their time-consuming nature. To mitigate these drawbacks, we propose a machine learning-driven framework for automated scoring of lung collagen content. Our study begins with the collection of a lung slide image dataset from adult female mice using second harmonic generation (SHG) microscopy. In our proposed approach, first, we manually extracted features based on the 46 statistical parameters of fibrillar collagen. Subsequently, we pre-processed the images and utilized a pre-trained VGG16 model to uncover hidden features from pre-processed images. We then combined both image and statistical features to train various machine learning and deep neural network models for classification tasks. We employed advanced unsupervised techniques like K-means, principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to conduct thorough image analysis for lung collagen content. Also, the evaluation of the trained models using the collagen data includes both binary and multi-label classification to predict lung cancer in a urethane-induced mouse model. Experimental validation of our proposed approach demonstrates promising results. We obtained an average accuracy of 83% and an area under the receiver operating characteristic curve (ROC AUC) values of 0.96 through the use of a support vector machine (SVM) model for binary categorization tasks. For multi-label classification tasks, to quantify the structural alteration of collagen, we attained an average accuracy of 73% and ROC AUC values of 1.0, 0.38, 0.95, and 0.86 for control, baseline, treatment_1, and treatment_2 groups, respectively. Our findings provide significant potential for enhancing diagnostic accuracy, understanding disease mechanisms, and improving clinical practice using machine learning and deep learning models.

Generic FPGA Pre-Processing Image Library for Industrial Vision Systems.

Currently, there is a demand for an increase in the diversity and quality of new products reaching the consumer market. This fact imposes new challenges for different industrial sectors, including processes that integrate machine vision. Hardware acceleration and improvements in processing efficiency are becoming crucial for vision-based algorithms to follow the complexity growth of future industrial systems. This article presents a generic library of pre-processing filters for execution in field-programmable gate arrays (FPGAs) to reduce the overall image processing time in vision systems. An experimental setup based on the Zybo Z7 Pcam 5C Demo project was developed and used to validate the filters described in VHDL (VHSIC hardware description language). Finally, a comparison of the execution times using GPU and CPU platforms was performed as well as an evaluation of the integration of the current work in an industrial application. The results showed a decrease in the pre-processing time from milliseconds to nanoseconds when using FPGAs.

Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer.

Desenvolvimento de metodologia para medição do fator output para blocos de elétrons

In light of the routine verification of output factor for electron blocks manufactured for patients and outside the standard square field measurements, this study proposes a methodology for measuring the output factor using EPID, validated by water measurements with a diode, and employing Python programming to analyze the pre-processed detector images. The study used square and rectangular blocks to validate the code and evaluate the limitations of the EPID, finding an agreement of up to ±3.0% for fields larger than 6.0 cm² and ±5.0% for fields between 3.0 cm² and 5.0 cm². The final evaluation aimed to compare square field measurements with asymmetric rectangular fields, and the results showed deviations of less than ±1.0%, thus indicating the feasibility of using EPID for this methodology.

Research on Weld Seam Marking Based on Machine Vision Technology

The rapid development of machine vision technology has made it widely used in various industrial fields. The detection of welding quality can also be achieved through this technology. The core of welding quality inspection is to realize the marking of weld features. To solve the problem of weld feature marking, the GUI weld marking interface is built based on MATLAB. After the process of image acquisition, image preprocessing, edge detection and feature extraction, the weld marking experiment of is finally completed. Based on data analysis of the experiment, it can be concluded that Canny edge detection operator has the best marking effect and the most stable detection for the research object used.

SAR-NTV-YOLOv8: A Neural Network Aircraft Detection Method in SAR Images Based on Despeckling Preprocessing

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

Optimized heterogeneous bi-directional recurrent neural network for early leaf disease detection and pesticides recommendation system

In this manuscript, optimized heterogeneous bi-directional recurrent neural network for early leaf disease detection and pesticides recommendation system (HBDRNN-ELD-PRS) is proposed. Initially, the input images are collected from plant dataset. To execute this, the collected input image is pre-processed using multimodal hierarchical graph collaborative filtering (MHGCF) for removing the noise, then the pre-processed images are fed to the feature extraction using second-order synchrony-extracting wavelet transform (SOSEWT) to extract the geometric features, such as area, slope, cancroids and perimeter. Then the extracted images are fed to the heterogeneous bi-directional recurrent neural network (HBDRNN) for effectively categorize Leaf Disease Detection as pepper bell bacterial spot, pepper bell healthy, potato late blight, potato early blight, potato healthy. Generally, HBDRNN does not adapt any optimization methods to compute optimal parameters to ensure accurate leaf diseases classification. Hence, the harbor seal whiskers optimization algorithm (HSWOA) is proposed to optimize the heterogeneous bi-directional recurrent neural network which accurately classifies the leaf disease. The proposed HBDRNN-ELD-PRS is implemented in Python. The performance metrics, such as accuracy, precision, specificity, recall, F1-score, computation time, ROC are analyzed. The proposed HBDRNN-ELD-PRS approach achieves 99.87% accuracy, 98.09% precision, and 97.83% recall when compared to the existing techniques.

Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions

Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions

Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification

Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. This study introduces a novel hyperparameter optimization strategy using the Manta Rays Foraging Optimizer (MRFO). A model tailored for skin cancer classification is created by fine-tuning a Convolutional Neural Network (CNN) with MRFO, coupled with in-depth image preprocessing. Empirical evaluations on diverse datasets (ISIC, PH2, HAM10000) showcase the significant superiority of the MRFO-based model over conventional optimization algorithms. The model achieves impressive accuracy and loss metrics (ISIC: 99.43 %, 0.0250; PH2: 99.96 %, 0.0033; HAM10000: 97.70 %, 0.0626), outperforming alternative optimization algorithms such as the Grey Wolf Optimizer (98.33 % accuracy, 0.17 loss), Whale Optimization Algorithm (96 % accuracy), Grasshopper Optimization Algorithm (97.2 % accuracy), Densnet121-MRFO (99.26 % accuracy), InceptionV3 with GA (99.9 % accuracy), and African Vulture Optimization Algorithm (92.7 % accuracy). The novel approach demonstrates superior accuracy and loss metrics, underscoring its potential for precise and efficient skin cancer detection. Additionally, narrow confidence intervals and balanced precision-recall confirm the model’s generalizability and effectiveness, paving the way for early and accurate skin cancer detection and potentially improving patient outcomes.

Terrain Recognition Using Deep Learning

This paper presents the method for a vision-based terrain recognition system that uses a convolutional neural network (CNN). The dataset includes images of various terrains under different conditions. The pretrained CNN is used for extracting features from pre-processed images, which are then used to classify the terrain types. The SoftMax layer of the architecture provides the result of terrain recognition. In the present work, experiments are performed with two pre-trained models. The system provides 80% accuracy using Tensorflow and ensemble learning Keywords— CNN; ; Tensorflow; terrain recognition; deep learning

Detection of bone cancer based on a four-phase framework generative deep belief neural network in deep learning

The medical image processing plays an important role in analyzing and detecting tumors and detecting cancer cells in their early stages. Microscopic images were used as a deep learning approach to recognize bone features. These images were acquired using microscopy, a time-consuming and labor-intensive process that required repeating. Cancer cells cannot be identified by conventional techniques because of the noise in the images. To address these challenges, this paper introduces the Gated Convolutional Neural Network (GCNN) techniques used to identify multiclass bone cancer, including non-tumor, non-viable, and viable tumors. As a first stage, the most basic part of image Preprocessing is to deposing without interrupting the diagnostics information during the removal of noise. The second stage is edge detection, used for image brightness discontinuity detection based on Scaled Region Edge Detector (SRED). It is used for image segmentation and data extraction within regions based on the Screen Cluster Area Segmentation (SCAS) to segment the Region of Interest (RoI) based on Relative Scalar Color Histogram (RSCH). The fourth step is Feature extraction is to find out the features and match the image feature from the Bone Cancer Detection object based on the maximum threshold weights using GCNN with Maximum Standard External Regions (MSER) used for features extracting applied to improve feature quality analysis and Feature support rate. This results in higher classification accuracy, Precision, recall under F-measure, and lower error rates. This enables a more achieved classification than other existing methods.

A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.

In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model then classifies the preprocessed images into distinct tumor types: glioma, meningioma, and pituitary tumor. Following classification, the RUNet model performs pixel-level segmentation to delineate tumor regions within the MRI scans. The FT-CNN leverages the VGG19 architecture, pre-trained on large datasets and fine-tuned for specific tumor classification tasks. Features extracted from MRI images are used to train the FT-CNN, demonstrating robust performance in discriminating between tumor types. Subsequently, the RUNet model, inspired by the U-Net design and enhanced with residual blocks, effectively segments tumors by combining high-resolution spatial information from the encoding path with context-rich features from the bottleneck. My experimental results indicate that the integrated pipeline achieves high accuracy in both classification (96%) and segmentation tasks (98%), showcasing its potential for clinical applications in brain tumor diagnosis. For the classification task, the metrics involved are loss, accuracy, confusion matrix, and classification report, while for the segmentation task, the metrics used are loss, accuracy, Dice coefficient, intersection over union, and Jaccard distance. To further validate the generalizability and robustness of the integrated pipeline, I evaluated the model on two additional datasets. The first dataset consists of 7023 images for classification tasks, expanding to a four-class dataset. The second dataset contains approximately 3929 images for both classification and segmentation tasks, including a binary classification scenario. The model demonstrated robust performance, achieving 95% accuracy on the four-class task and high accuracy (96%) in the binary classification and segmentation tasks, with a Dice coefficient of 95%.

RvXmBlendNet: A Multi-architecture Hybrid Model for Improved Skin Cancer Detection

AbstractSkin cancer, one of the most dangerous cancers, poses a significant global threat. While early detection can substantially improve survival rates, traditional dermatologists often face challenges in accurate diagnosis, leading to delays in treatment and avoidable fatalities. Deep learning models like CNN and transfer learning have enhanced diagnosis from dermoscopic images, providing precise and timely detection. However, despite the progress made with hybrid models, many existing approaches still face challenges, such as limited generalization across diverse datasets, vulnerability to overfitting, and difficulty in capturing complex patterns. As a result, there is a growing need for more robust and effective hybrid models that integrate multiple architectures and advanced mechanisms to address these challenges. Therefore, this study aims to introduce a novel multi-architecture hybrid deep learning model called "RvXmBlendNet," which combines the strengths of four individual models: ResNet50 (R), VGG19 (v), Xception (X), and MobileNet (m), followed by "BlendNet" to signify their fusion into a unified architecture. The integration of these models is achieved through a synergistic combination of architectures, incorporating self-attention mechanisms using attention layers and adaptive content blocks. This study used the HAM10000 dataset to refine dermoscopic image preprocessing and enhance deep learning model accuracy. Techniques like OpenCV-based hair removal, min–max scaling, and adaptive histogram equalization were employed to improve image quality and feature extraction. A comparative study between the proposed hybrid "RvXmBlendNet" and individual models (CNN, ResNet50, VGG19, Xception, and MobileNet) demonstrated that "RvXmBlendNet" achieved the highest accuracy of 98.26%, surpassing other models. These results suggest that the system can facilitate earlier interventions, improve patient outcomes, and potentially lower healthcare costs by reducing the need for invasive diagnostic procedures.

Development of Image Pre-Processing Methods for Software Compensation of Anomal Refraction of the Observer’s Eyes

In recent decades, the practice of demonstrating various static and video images to users using digital, processor-controlled, most often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) has spurred the development of various methods to improve the perception of such images by means of computerized image preprocessing. This also applies to methods of preprocessing images shown to users with various refractive anomalies of the eye(s) (e.g., myopia or astigmatism) in situations where they are not armed with glasses or other corrective devices. Over the past 20+ years, researchers have published dozens of papers on this task, referred to as the precompensation task. In our opinion, the time has come to reflect on the development of scientific thought in this direction and to highlight the most important milestones in realizing the problems on the way to achieving “ideal” precompensation and in approaches to their successful solution. This is the focus of the first part of this review. In the second part, we focus on the current state of research in the stated area, highlight the problems not solved so far, and try to catch the trends of further development of image precompensation methods, paying maximum attention to neural network approaches.

Improving Palimpsest Readability via Image Preprocessing: An Investigation into Adjustment Techniques

The palimpsests represent unique historical sources that hold the potential for new insights in the field of human history. These manuscripts, rewritten and reused over time, pose challenges in the research related to their readability and interpretation. The present study aims to investigate the readability of palimpsests through the use of image preprocessing techniques. The article focuses on methods for the preprocessing of palimpsests that could lead to a significant improvement in the readability of the 'hidden' text. The challenges encountered during the processing of palimpsests are explored and various techniques applicable to improving the readability of these manuscripts are analyzed. The primary goal of preprocessing in this context is to separate the 'hidden' text from the visible one, neutralizing material defects and aging. The article presents specific methods such as extracting specific color range from a palimsest image. The experimental techniques are highlighted with sample codes illustrating the application of the respective technology. The current research attempts to advance the development of methods for processing palimpsests and opens up new perspectives for extracting information from those historically valuable manuscripts.

KLASIFIKASI PENYAKIT DAUN TOMAT DENGAN METODE CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN ARSITEKTUR INCEPTION-V3

Tomato is an important food crop in the world, including Indonesia, which often faces leaf diseases such as mosaic virus, target spot, bacterial spot, yellow leaf curl virus, late blight, leaf mold, early blight, spider mites, and septoria leaf spot. These diseases are difficult to recognize manually, so Deep Learning technology, especially Convolutional Neural Network (CNN), is used for the classification process. This research uses Inception V3 transfer learning model and image preprocessing and data augmentation. Testing is done with various optimizers (Adam, SGD, RMS Prop) to find the best model. Tests were conducted using confusion matrix to determine the level of accuracy produced by the CNN model. The results show an accuracy of 93.8% with the optimal model using Optimizer Adam.

Lung disease prediction based on CT images using REInf-net and world cup optimization based BI-LSTM classification

ABSTRACT A major global source of disability as well as mortality is respiratory illness. Though visual evaluation of computed tomography (CT) images and chest radiographs are a primary diagnostic for respiratory illnesses, it is limited in its ability to assess severity and predict patient outcomes due to low specificity and fundamental infectious organisms. In order to address these problems, world cup optimization-based Bi-LSTM classification and lung disease prediction on CT images using REINF-net were employed. To enhance the image quality, the gathered lung CT images are pre-processed using Lucy Richardson and CLAHE algorithms. For the purpose of lung infection segmentation, the pre-processed images are segmented using the REInf-net. The GLRLM method is used to extract features from the segmented images. In order to predict lung disease in CT images, the extracted features are trained using the Bi-LSTM based on world cup optimization. Accuracy, Precision, recall, Error and Specificity for the proposed model are 97.8%, 96.7%, 96.7%, 2.2% and 98.3%. These evaluated values are contrasted with the results of existing methods like WCO-BiLSTM, MLP, CNN and LSTM. Finally, the Lung disease prediction based on CT images using REINF-Net and world cup optimization based BI-LSTM classification performs better than the existing model.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921. The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Automatic classification of bird species related to power line faults using deep convolution features and ECOC‐SVM model

AbstractBird‐related outages greatly threaten the safety of overhead transmission and distribution lines, while electrocution and collisions of birds with power lines, especially endangered species, are significant environmental concerns. Automatic bird recognition can be helpful to mitigate this contradiction. This paper proposes a method for automatic classification of bird species related to power line faults combining deep convolution features with error‐correcting output codes support vector machine (ECOC‐SVM). An image dataset of about 20 high‐risk and 20 low‐risk bird species was constructed, and the feed‐forward denoising convolutional neural network was used for image preprocessing. The deep convolution features of bird images were extracted by DarkNet‐53, and taken as inputs of the ECOC‐SVM for model training and bird species classification. The gradient‐weighted class activation mapping was used for visual explanations of the model decision region. The experimental results indicate that the average accuracy of the proposed method can reach 94.39%, and its performance was better than other models using different feature extraction networks and classification algorithms.