Contrast Limited Adaptive Histogram Equalization Research Articles

Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.

Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes.

Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Underwater images can be captured either with the help of light waves or sound waves. Images that are taken underwater typically are not of optimum quality as they suffer from issues such as low contrast, blurring of detail, colour distortion, and greenish tones. Several physical processes that take place in the aquatic environment, such as light absorption, refraction, and scattering, are responsible for the existence of such degradation in underwater images. To address these challenges, numerous researchers have put forth a range of cutting-edge techniques for enhancing and restoring such degraded underwater images, with the aim of addressing these issues. These techniques primarily focus on improving visibility and enhancing the level of detail. To achieve this, we propose a method that performs White Balancing in the LAB colour space to remove the bluish-greenish tones present in the image. Next, we enhance the contrast by first converting the RGB image into HSV and HLS colour spaces and then by using the S & V channels in HSV and L & S colour channels in HLS, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE). To control the brightness of the enhanced image, we apply Gamma Correction. Lastly, by using the method Dark Channel Prior (DCP), we separate the image’s red channel from the RGB colour space and perform the dehazing operation to get the final enhanced image. We have conducted a comprehensive qualitative analysis of our proposed approach as well as existing techniques, evaluating them objectively and subjectively through metrics such as peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE), structural similarity (SSIM), and the underwater colour image quality evaluation metric (UCIQE) and underwater image quality measure (UIQM). Since our proposed approach uses traditional image processing methods, it is computationally less expensive and quicker as compared to deep learning or frequency domain-based methods. With this, it can be adapted for using in real-time applications such as underwater navigation, examination of the behavior of marine ecosystems and other scientific research.

Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex

Aiming at solving the problems of local halo blurring, insufficient edge detail preservation, and serious noise in traditional image enhancement algorithms, an improved Retinex algorithm for low-light mine image enhancement is proposed. Firstly, in HSV color space, the hue component remains unmodified, and the improved multi-scale guided filtering and Retinex algorithm are combined to estimate the illumination and reflection components from the brightness component. Secondly, the illumination component is equalized using the Weber–Fechner law, and the contrast limited adaptive histogram equalization (CLAHE) is fused with the improved guided filtering for the brightness enhancement and denoising of reflection component. Then, the saturation component is adaptively stretched. Finally, it is converted back to RGB space to obtain the enhanced image. By comparing with single-scale Retinex (SSR) algorithm and multi-scale Retinex (MSR) algorithm, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) are improved by an average of 50.55%, 19.32%, 3.08%, 28.34%, 29.10%, and 22.97%. The experimental dates demonstrate that the algorithm improves image brightness, prevents halo artifacts while retaining edge details, reduces the effect of noise, and provides some theoretical references for low-light image enhancement.

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.

Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification

Deep learning provides many convenient methods to help medical practitioners take informed decisions about diverse ailments. The goal of this project is to measure the effectiveness of filters and contrast enhancement techniques qualitatively and quantitatively in classifying lung scan images. Transfer deep learning was used to obtain the necessary results, with DenseNet 121 being the base model. Salt and pepper filter was used to introduce noise, and 3×3 mean and 5×5 mean with contrast limited adaptive histogram equalization (CLAHE) was used to minimize the effect of noise. All layers excluding the rearmost were frozen, and new dense and dropout layers were added to identify features of computerized tomography (CT) scan images of lungs. The resultant models were of comparable accuracy, where the model with no filter gave the accurate results for the given data, and the one using the 5×5 mean filter gave better adaptability in classification of unseen data. The misclassification between normal and pneumonia affected lungs is relatively higher, because of the lack of distinct features between them.

Detection of Illegal Goods using X-ray Image Enhancement Algorithm

Abstract: In order to facilitate the security personnel to identify the items in the X-ray image, it is necessary to enhance the security X-ray image to highlight the color, edge, shape and other details of the different items in the image, so that the security personnel can identify the inspected items more accurately and quickly, to ensure the safe operation of the airport. Aiming at the problem of color distortion in CLAHE enhanced airport security X-ray images, an X-ray image enhancement algorithm combining USM+CLAHE is proposed. In this work, First, we calculate the grayscale images on the R, G, and B channels of the X-ray image and perform Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement respectively, and then merge the enhanced R, G, and B grayscale images. Next, we perform the Unsharp Mask (USM) sharpening operation on the Xray image enhanced by CLAHE, and finally merge the original image and the USM sharpened image according to the weight. The experimental results show that the USM+CLAHE algorithm can effectively enhance the security X-ray image, and at the same time can suppress the color distortion of the enhanced image.

Classification of celiac disease using novel approach of three-level DWT decomposition and linear support vector machine

In the medical field, the requirement for automated medical diagnosis software has risen as the application uses machine learning to facilitate health analysis from different data points from a patient comparing it with massive amounts of medical data to diagnose and prevent disease. The automated system can run multiple tests simultaneously and thus resulting to faster turnaround time and enables timely patient care along with higher accuracy and reliability. The irregularities in the small intestine villi’s structure cause various autoimmune disorders. So, this work aims to find a novel technique for the feature extraction of upper endoscopy images of celiac disease. We employed CLAHE (Contrast Limited Adaptive Histogram Equalization) for the preprocessing step and the Sobel operator with gradient magnitude for the segmentation of the enhanced image. In this manuscript, we have proposed a novel approach by calculating texture features through gray level co-occurrence matrix and frequency analysis using 3-level discrete wavelet transform decomposition on endoscopy images that renders novelty to the work. Thereafter, linear SVM (support vector machine) with PCA (Principal Component Analysis) is used for classification. The ensemble approach attains accuracy, sensitivity, and specificity of 78.49 %, 90.32 %, and 77.27% respectively. The outcomes achieved with the suggested approach are compared with some state-of-the-art methods using the same dataset. The results are promising due to high sensitivity for the treatment of untreated celiac disease and can prove boom to the medical industry by assisting clinicians to diagnose the disease at an early stage.

A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning

To assess the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) and Vertex AI in executing code-free preprocessing, training machine learning (ML) models, and analyzing the data. Evaluation of diagnostic test or technology. ChatGPT and Vetrex AI as publicly available large language model and ML platform, respectively. ChatGPT was employed to improve the resolution of fundus photography images from the Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (Messidor-2) open-source dataset using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique by Fiji software. Subsequently, Vertex AI, an automated ML (AutoML) platform, was utilized to develop 2 classification models. The first model served as a binary classifier for detecting the presence of diabetic retinopathy (DR), while the second determined its severity. Finally, ChatGPT was used to provide scripts for R and Python programming languages for data analysis and was also directly employed in analyzing the data in a code-free method. Evaluating the utility of ChatGPT in generating scripts for preprocessing images using Fiji and analyzing data across Python and R and assessing its potential in analyzing data through a code-free method. Investigating the capabilities of Vertex AI to train image classification models for detection of DR and its severity. Two ML models were trained using 1740 images from the Messidor-2 database. The first model, designed to detect the severity of DR, achieved an area under the precision-recall curve (AUPRC) of 0.81, with a precision rate of 81.81% and recall of 72.83%. The second model, tailored for the detection of the presence of DR, recorded a precision and recall of 84.48% with an AUPRC of 0.90. ChatGPT and Vertex AI have the potential to enable physicians without coding expertise to preprocess images, analyze data, and train ML models. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Multi-scale fusion for image enhancement in shield tunneling: a combined MSRCR and CLAHE approach

Aiming at addressing the complex lighting conditions in shield construction environments and enhancing the contrast of low-light images while preserving image details, this paper presents an image enhancement technique based on multi-scale fusion. The proposed method first duplicates the input image into two copies. Subsequently, the first copy undergoes color and contrast adjustment via the adaptive weighted Retinex algorithm for color restoration (A-MSRCR) algorithm. Meanwhile, the second copy undergoes contrast enhancement and detail preservation by combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, a normalized weight map is introduced to facilitate multi-scale fusion of the two enhanced images, ensuring an overall enhancement in both contrast and detail. The experimental results show that the proposed method provides better visual quality and objective data than the compared methods. Moreover, it offers valuable support for subsequent recognition and localization tasks.

A New Method Used to Enhance the SPAIR Image of the Spine MRI.

Magnetic resonance imaging (MRI) is a handy diagnostic tool for orthopedic disorders, particularly spinal and joint diseases. The lumbar intervertebral disc is visible in the T1 and T2 weight sequences of the spine MRI, which aids in diagnosing lumbar disc herniation, lumbar spine tuberculosis, lumbar spine tumors, and other conditions. The lumbar intervertebral disc cannot be seen accurately in the Spectral Attenuated Inversion Recovery (SPAIR) due to weaknesses in the fat and frequency offset parameters, which is not conducive to developing the intelligence diagnosis model of medical image. In order to solve this problem, we propose a composite framework, which is first to use the contrast limited adaptive histogram equalization (CLAHE) method to enhance the SPAIR image contrast of the spine MRI and then use the non-local means method to remove the noise of the image to ensure that the image contrast is uniform without losing details. We employ the Information Entropy (IE), Peak signal-to-noise ratio (PSNR), and feature similarity index measure (FSIM) to quantify image quality after enhancement by the composite framework. The outcomes of the experiments' output images and quantitative data indicate that our composite framework is better than others.

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

An introduction to double stain normalization technique for brain tumour histopathological images

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

BackgroundCOVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time.ResultsIn this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient.ConclusionOur proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

Enhanced model to detection of Diabetic Retinopathy using Deep Learning techniques

Diagnosing and treating diabetic retinopathy (DR) early on can prevent vision loss. None, moderate, mild, proliferate, and severe are the top five DR phases. This work presents a deep learning (DL) model that identifies all five stages of DR more accurately than earlier approaches. the suggested method with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and circular mask with using random search for hyperparameter. The next step was using augmentation techniques to create a balanced dataset using the same parameters for both scenarios. The created model outperformed previous techniques for identifying the five stages of DR, with an accuracy of 90% , using Efficient net B3 applied to the Asia Pacific TeleOphthalmology Society (APTOS) datasets.

Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration.

This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

YOLOv8 Tabanlı Derin Öğrenme Teknikleri Yoluyla Giardia İntestinalis Görüntü Algılamanın Geliştirilmesi

Giardia intestinalis (G. intestinalis), a parasitic organism that causes gastrointestinal infections, represents a huge challenge in precisely identifying species from microscopic images. The complexities of accurate diagnosis and treatment require a shift towards automated solutions that enhance diagnostic efficiency and accuracy. In this study, we take advantage of the YOLOv8 deep learning model, comparing its performance with traditional methods, to enhance Giardia intestinalis detection.Our dataset, which has been carefully obtained by Burdur Mehmet Akif Ersoy University Faculty of Veterinary Medicine, Department of Pathology adds a unique dimension to our research. The dataset consists of 264 images of G. İntestinalis and is subjected to preprocessing with RGB/grayscale filters and contrast-limited adaptive histogram equalization for optimal model input.Deep learning architectures tested, including YOLOv8, show an accuracy rate of 95%. Notably, the YOLOv8 model shows promising results, indicating its potential to transform the diagnosis of G. intestinalis. Beyond immediate application, our research paves the way for the integration of YOLOv8 into broader healthcare contexts, promising effective tools for managing G. İntestinalis infections.Furthermore, our study allows the transfer of G. İntestinalis diagnostic expertise from expert veterinarians to the AI model. Veterinarians working in this field can now obtain preliminary diagnostic information through a mobile application. This innovative approach enhances the competence of veterinarians and expands their experience in this field.This research significantly pushes the boundaries in G. İntestinalis image analysis but also puts the foundation for the broader use of advanced deep learning techniques in medical applications. The implications of our findings extend beyond G. İntestinalis diagnosis, providing insight into the transformative impact of YOLOv8 in medical and biological image analysis. Our study opens the way for future developments, shaping the path of intelligent computer vision methods in real-world medical applications.

Haze Removal Of Underwater Images Using Fusion Technique

Problems with water absorption and dispersion, color loss, poor light, and limited vision are some of the main obstacles and restrictions in underwater photography. A fusion-based method is suggested to enhance these photos' quality; specifically, it would employ the Gray world algorithm and CLAHE (Contrast Limited Adaptive Histogram Equalization) to boost color and contrast. The contrast-enhanced, white-balanced, and weight maps are successfully fused using a multi-scale fusion approach to provide a superior picture. This technique greatly improves underwater picture clarity by eliminating haze and increasing visibility.

An enhanced tunicate swarm algorithm with deep-learning based rice seedling classification for sustainable computing based smart agriculture

&lt;abstract&gt; &lt;p&gt;Smart agricultural techniques employ current information and communication technologies, leveraging artificial intelligence (AI) for effectually managing the crop. Recognizing rice seedlings, which is crucial for harvest estimation, traditionally depends on human supervision but can be expedited and enhanced via computer vision (CV). Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras bestow a swift and precise option for crop condition surveillance, specifically in cloudy states, giving valuable insights into crop management and breeding programs. Therefore, we improved an enhanced tunicate swarm algorithm with deep learning-based rice seedling classification (ETSADL-RSC). The presented ETSADL-RSC technique examined the UAV images to classify them into two classes: Rice seedlings and arable land. Initially, the quality of the pictures could be enhanced by a contrast limited adaptive histogram equalization (CLAHE) approach. Next, the ETSADL-RSC technique used the neural architectural search network (NASNet) method for the feature extraction process and its hyperparameters could be tuned by the ETSA model. For rice seedling classification, the ETSADL-RSC technique used a sparse autoencoder (SAE) model. The experimental outcome study of the ETSADL-RSC system was verified for the UAV Rice Seedling Classification dataset. Wide simulation analysis of the ETSADL-RSC model stated the greater accuracy performance of 97.79% over other DL classifiers.&lt;/p&gt; &lt;/abstract&gt;

Classification of Pancreatic Cystic Lesions Using ResNet Deep Learning Network in Confocal Laser Endomicroscopy Videos

Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall.

Effectual Augmentation of Glaucoma Prediction in Retinal Fundus Images using Hybrid Level Fusion of Image Pre-Processing Techniques

Glaucoma is a condition where the eyes of human beings are infected due to retinal damage which could result in loss of vision. It generally occurs due to prolonged pressure on the eye and affects the optic nerve if not treated at the earliest stage. However, it is hard for even experts to detect it at the earlier stage. Hence numerous image processing techniques were applied to identify Glaucoma in retinal eyes. The profound purpose of the work is to propose a pre-processing console to remove outliers in the Glaucoma retinal Fundus images using Denoising techniques of pre-processing to enhance the prediction using image pre-processing and computer vision techniques. The model was created with three stages including applying the denoising model using the Median Filtering for Edge Preservation, Contrast Limited Adaptive Histogram Equalization (CLAHE) and optimizing by eliminating irrelevant features using the Black Widow Optimization model and finally evaluating the performance of denoising techniques using accuracy-based predictions. The results showed that after performing a combination of denoising and optimizing techniques, the image quality was enhanced with 97% outperforming the existing models.