Contrast Limited Adaptive Histogram Equalization Research Articles

Iridology based human health conditions predictions with computer vision and deep learning

In today’s world, ocular diseases have become widespread. The “ocular illness” refers to any abnormality, impairment, or dysfunction of vision that impacts the eye. The most common forms of ocular disease include dry eyes, conjunctivitis, blepharitis, and glaucoma. Each of these conditions can cause redness, pain, and blurry vision. To resolve the challenges in human ocular diseases, the need for detection becomes an important part of every person’s life. There are numerous approaches by various researchers to detect and predict the disease early, but the previous approaches required certain improvements to predict the ocular disease more accurately and easily. In the proposed method, deep learning-based ocular disease prediction with high accuracy and low cost is implemented. The framework deals with the collection of data from the diabetes-iridology dataset and pre-processing using the Contrast limited adaptive histogram equalization (CLAHE). The Attenuation-based U-net is the most advanced proposed method used for the segmentation of ocular disease with higher accuracy. The Adaptive Residual Squeeze and Excitation network (ARSEnet) is the proposed method used for a better classification process. Based on the evaluation results, the proposed technique better classified eye illness. The Python tool is used to carry out the recommended method. The proposed strategy outperformed others in terms of accuracy, precision, specificity, recall, and f1 score. The accuracy rate of the proposed method is 97.02%. The resultant analysis clearly proves that the proposed system provides the best accuracy for predicting ocular disease.

Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application.

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model's performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings.

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.

SegNet‐VOLO model for classifying microplastic contaminants in water bodies

AbstractIn recent times, microplastics (MPs) have emerged as notable contaminants within several environments, especially in water bodies. The characterization and description of MPs necessitate extensive and laborious analytical methods, making this part of MPs research an essential issue. In this research, SegNet‐Vision Outlooker (VOLO), a computer vision and deep learning (DL)‐based model, is proposed for detecting and classifying MPs present in a water environment. This research model includes step‐by‐step processes such as data collection, preprocessing, filtering and enhancement, augmentation, segmentation, feature extraction, and classification for detecting MPs. The key objective of this research model is to improve the classification accuracy in detecting MPs and to validate the model's effectiveness in handling holographic images. The Holographic Image MPs dataset is collected and used to evaluate the model. In preprocessing, image rescaling is performed to match the proposed model's input resolution as 224 × 224. After rescaling, the images are applied to remove noise using a bilateral filtering technique. The contrast‐limited adaptive histogram equalization (CLAHE) method is applied to enhance the image with better contrast and brightness, which helps the model to segment and classify the images accurately. The enhanced images are applied to the SegNet model for segmentation, which segmented the images according to the MP classes. Based on the segmented images, the VOLO‐D1 model extracted the features and classified the images to detect the MPs present in the images. The SegNet‐VOLO model attained 97.70% detection rate, 98.26% accuracy, 98.13% F1‐score, and 98.62% precision. These performances are compared with the various existing models discussed in the review, where the research model outperformed all the models with better performances.

Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation.

In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures. This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle. The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE. Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.

Hand Gestures Recognition Model Using Adaptive Feature Extraction with Attention-Based Hybrid Deep Learning via Optimization Strategy

Research on continuous hand gesture recognition is motivated by applications of deaf people to provide effective communication between one another. Numerous techniques are adapted for effectively identifying the hand gesture. Moreover, the collection of data becomes a challenging task in the existing models. In some instances, hand gesture in low-resolution images becomes a complicated task. In this research work, a deep learning-based hand gesture detection model is designed using hand data points to uncover patterns that are used to convey information. Initially, hand gesture images are obtained from public sources. The collected inputs are preprocessed through Contrast-Limited Adaptive Histogram Equalization and filtering to eliminate the unnecessary blurs in the images. The preprocessed images undergo hand segmentation using four methods color space transformation, skin color detection, active contour, and morphological operation. The segmented image is subjected to data point feature extraction, where the Adaptive Weighted Scale-Invariant Feature Transform is utilized for further enhancement. The hand data point-extracted features are given to the recognition of hand gestures with the support of an attention-based hybrid network, where the Attention-based Hybrid 1D Convolutional Neural Network with Recurrent Neural Network recognizes the hand gestures from the hand data points. In the hybridization network, the values are optimized with the help of the investigated Adaptive Dandelion White Shark Optimizer to enhance recognition effectiveness. The test results are validated with the existing hand gesture recognition models using diverse evaluation metrics. The findings of the recommended method show 97% and 99% in terms of accuracy and NPV. Thus, the developed model is validated to outperform significant performance rather than the existing models.

Edge-protected IDW-based DEM detail enhancement and 3D terrain visualization

Terrain visualization plays an important role in Digital Twin, geology, environmental science, and exploration. Generating 3D visualization results by the Digital Elevation Model (DEM) is an effective method. However, existing DEM 3D visualization methods generally lack sufficient detail to obtain good visual effects. Therefore, we propose a novel DEM detail enhancement and 3D terrain visualization method. Our method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and the edge-protected Inverse Distance Weighted (IDW) algorithm, and achieves 3D visualization of DEM data through a workflow of converting the raw DEM data into an image, followed by enhancement and detail optimization of the image, and finally converting the image into triangular meshes. The edge-protected IDW algorithm performs IDW interpolation optimization based on the Canny edge detector combined with the Otsu threshold optimization method to detect edge areas, achieving smooth transitions in the edge areas. Experiments show that our method generates detail-rich 3D visualization results after enhancing original DEM data. We also conducted an ablation study to provide a side-by-side comparison of each step. Our method can be further applied to many tasks, such as virtual reality, GIS, and geological exploration.

Xception Taylor Cascade Neuro Network based infection level identification of tuberculosis using sputum images

Currently, one amongst most primary health problems and an enormously transmittable disease is Tuberculosis (TB). This disease spreads all over the world and is commonly developed by Mycobacterium TB (MTB). TB causes fatality if it is not identified at earlier stages. Thus, accurate and effectual model is necessary for detecting infection level of TB. Here, Xception Taylor Cascade Neuro Network (Xception T-Cascade NNet) is presented for infection level identification of TB utilizing sputum images. Firstly, input sputum image acquired from certain database is pre-processed by denoising and histogram equalization utilizing contrast limited adaptive histogram equalization (CLAHE). SegNet is utilized for bacilli segmentation and it is tuned by White Shark Optimizer (WSO). Thereafter, suitable features such as designed discrete cosine transform (DCT) with angled local directional pattern (ALDP), statistical features, shape features and gray-level co-occurrence model (GLCM) texture features are extracted for further processing. Lastly, infection level identification of TB is conducted by Xception T-Cascade NNet. However, Xception T-Cascade NNet is an integration of Xception with Cascade Neuro-Fuzzy Network (NFN) by Taylor concept. In addition, Xception T-Cascade NNet achieved 88.5% of accuracy, 90.8% of true negative rate (TNR) and 89.4% of true positive rate (TPR) and as well as minimal false negative rate (FNR) of 0.092 and false positive rate (FPR) of 0.106.

A novel automated approach for fish biomass estimation in turbid environments through deep learning, object detection, and regression

Estimating fish biomass is crucial in the fisheries sector, where traditional methods often harm fish through manual sampling and anesthetics. A non-invasive approach is introduced using underwater films to estimate fish biomass in turbid conditions. This study presents the “Aquatic WeightNet” dataset, targeting the Genetically Improved Formed Tilapia (GIFT) Tilapia species, and addresses the challenge of unclear images with preprocessing techniques like dehazing and Contrast Limited Adaptive Histogram Equalization (CLAHE). YOLOv8, a leading object detection model modified to accommodate the custom Aquatic WeightNet dataset's varied image sizes with five detection heads, P2 to P6, is employed, achieving a recall of 0.997 and a mean Average Precision (mAP) of 0.899 within the 50–95% Intersection over Union (IoU) range. Fish biomass estimation assesses depth, length, and width using regression models for calculation. A three-phase grid search identifies the most effective models, with the Extra Trees Regressor outperforming depth estimation with mean absolute error (MAE) of 0.63 and coefficient of determination (R2) of 0.87 and the Random Forest Regressor for length and width (MAE of 0.01 and R2 of 0.99). For biomass estimation, the Extra Trees Regressor again performs well (MAE of 0.004 and R2 of 0.99), which is critical for determining optimal feed quantities to enhance aquaculture efficiency. This study emphasizes a non-invasive method to estimate fish biomass, optimizing the effectiveness and ecological sustainability of fish farming in murky waters through advanced detection algorithms and robust regression models.

Improving the Quality and Performance of Underwater Image Classification using the CLAHE-CNN Method

Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolphins, sea rays, and whales in the underwater imagery dataset. The experiment was conducted using Python programming language on a computer that had 24GB RAM, Intel® Core™ i7-10510U CPU and hardware properties of Intel® HD Graphics graphics card. The results of this study show how CLAHE improved the CNN classification of underwater imagery by 0.91% in training data, 0.45% in validation data, and 2.02% in test data.

Optimization of intelligent guided vehicle vision navigation based on improved YOLOv2.

Addressing the challenge of limited accuracy and real-time performance in intelligent guided vehicle (IGV) image recognition and detection, typically reliant on traditional feature extraction approaches. This study delves into a visual navigation detection method using an improved You Only Look Once (YOLO) model-simplified YOLOv2 (SYOLOv2) to satisfy the complex operating conditions of the port and the limitations of IGV hardware computing. The convolutional neural network structure of YOLOv2 is refined to ensure adaptability to varying weather conditions using a single image. Preprocessing of images involves Contrast Limited Adaptive Histogram Equalization (CLAHE), while an adaptive image resolution detection model, contingent upon vehicle speed, is proposed to enhance the detection performance. The comparative experiments conducted on image datasets reflective of actual road conditions and weather conditions demonstrate notable enhancements in accuracy and frames transmitted per second compared to conventional methods. These improvements signify the efficacy of the proposed approach in meeting the stringent requirements for real-time detection on IGV platforms.

Self-Trained Convolutional Neural Network (CNN) for Tuberculosis Diagnosis in Medical Imaging.

Background Tuberculosis (TB) is a serious infectious disease that primarily affects the lungs.Despite advancements in the medical industry, TBremains a significant global health challenge. Early and accurate detection of TB is crucial for effective treatment and reducing transmission. This article presents a deep learning approach using convolutional neural networks (CNNs) to improve TB detection in chest X-ray images. Methods For the dataset, we collected 7000 images from Kaggle.com, of which 3500 exhibit tuberculosis evidence and the remaining 3500 are normal. Preprocessing techniques such as wavelet transformation, contrast-limited adaptive histogram equalisation (CLAHE), and gamma correction were applied to enhance the image quality. Random flipping, random rotation, random resizing, and random rescaling were among the techniques employed to increase dataset variability and model robustness. Convolutional, max-pooling, flatten, and dense layers comprised the CNN model architecture. For binary classification, sigmoid activation was utilised in the output layer and rectified linear unit (ReLU) activation in the input and hidden layers. Results The CNN model achieved an accuracy of ~96.57% in detecting TB from chest X-ray images, demonstrating the effectiveness of deep learning, particularly CNNs, in this application. Self-trained CNNs have optimised the results as compared to the transfer learning of various pre-trained models. Conclusion This study shows how well deep learning-in particular, CNNs-performs in the identification of tuberculosis. Subsequent efforts have to give precedence to optimising the model by obtaining more extensive datasets from the local hospitals and localities, which are vulnerable to TB, and stress the possibility of augmenting diagnostic knowledge in medical imaging via machine learning methodologies.

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Electric motors are essential equipment widely employed in various sectors. However, factors such as prolonged operation, environmental conditions, and inadequate maintenance make electric motors prone to various failures. In this study, we propose a thermography-based motor fault detection method based on InceptionV3 model. To enhance the detection accuracy, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input images. Furthermore, we improved the performance of the InceptionV3 by integrating a Squeeze-and-Excitation (SE) channel attention mechanism. The proposed model was tested using a dataset containing 369 thermal images of an electric motor with 11 types of faults. Image augmentation was employed to increase the data size and the evaluation was conducted using fivefold cross validation. Experimental results indicate that the proposed model can achieve accuracy, precision, recall, and F1 score of 98.82%, 98.93%, 98.82%, and 98.87%, respectively. Additionally, by freezing the fully connected layers of the InceptionV3 model for feature extraction and training a Support Vector Machines (SVM) to perform classification, it is able to achieve 100% detection rate across all four evaluation metrics. This research contributes to the field of industrial motor fault diagnosis. By incorporating deep learning techniques based on InceptionV3 and SE channel attention mechanism with a traditional classifier, the proposed method can accurately classify different motor faults.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity.

Sailfish optimizer based CLAHE with U-NET for MRI brain tumour segmentation

Semantic brain tumour segmentation is a significant contribution to medical image processing. This research is helpful for doctors making diagnoses and determining the severity of lesions. In recent years, convolutional neural networks have sometimes been known as self-remarkable representations in computer vision tasks. In three-dimensional medical image processing, CNN with architecture and skip connections has become increasingly popular. However, they are inadequate at both acquiring nearby and far-off semantic knowledge. However, the transformer's use of a vision. This accomplishment was made possible by the global information modelling capabilities of the transformer. When tryingto perform challenging prediction tasks, such as segmenting 3D medical pictures, it is vital to consider local and global features. In this study, we propose a hypothesis that predicts pre-processing steps that enhance the image quality and significantly impact accuracy with any statistical image classification method. We used a better picture enhancement methodology to back up our idea. This method has three unique stages: a median filter to eliminate noise, a histogram equalisation technique to boost contrast, and a grayscale-to-RGB conversion. The clipping limit of contrast-limited Adaptive Histogram Equalisation (CLAHE)is calculated by the Sailfish Optimization Algorithm (SOA) to achieve optimal picture enhancement. This study classifies semantically segmenting 3D brain tumours as a sequence-to-sequence prediction problem. U-net is proposed in the article as a novel method for segmenting 3D medical images; it incorporates a structure. A new technique for segmenting 3D medical images, U-net, is emerging. We trained and assessed our method on the BRATS 2019 dataset, which was found to outperform state-of-the-art algorithms and be competitive under similar settings.

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

Objectives: The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. Methods: In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which includes Gaussian filtering for noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance image features. In the second step, we identify tumor-related clusters using morphological operations and delineate the tumor regions using Active Contour (Snake) model to get a segmented image. In the final step, the Boltzmann Monte Carlo method is used to refine the edges of the segmented image. To evaluate the effectiveness of this approach, the 2D brain tumor datasets, available in the public domain, are used. The first dataset is taken from Kaggle website and has 3064 MRI human brain images and its respective ground truth images which is used for segmentation. The second dataset is used for visualization of segmented tumor, available in the same Kaggle website. Findings: The Performance metrics for finding similarity between the segmented images generated using the proposed MACB model and the ground truth images, available in the first dataset, exhibit higher values. That is, the proposed method has achieved higher values of Dice Similarity Coefficient (DSC): 93.26%, Jaccard Co-efficient: 86.44%, Sensitivity: 97.27%, Specificity: 99.43% and Pixel accuracy: 98.95%. Novelty: In this research work, MACB model is proposed for the detection, segmentation, and refinement process of brain tumor by incorporating Boltzmann Monte Carlo method with Morphological Region-Based Active Contour model. This novel approach has resulted in enhanced precision and efficiency in the brain tumor segmentation process. Keywords: Brain Tumor Segmentation, Morphological Operation, Active Contour, Boltzmann Monte Carlo Method, Magnetic Resonance Imaging

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification

The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain–computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel’s band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy; 14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.

Retinex Jointed Multiscale CLAHE Model for HDR Image Tone Compression

Tone-mapping algorithms aim to compress a wide dynamic range image into a narrower dynamic range image suitable for display on imaging devices. A representative tone-mapping algorithm, Retinex theory, reflects color constancy based on the human visual system and performs dynamic range compression. However, it may induce halo artifacts in some areas or degrade chroma and detail. Thus, this paper proposes a Retinex jointed multiscale contrast limited adaptive histogram equalization method. The proposed algorithm reduces localized halo artifacts and detail loss while maintaining the tone-compression effect via high-scale Retinex processing. A performance comparison of the experimental results between the proposed and existing methods confirms that the proposed method effectively reduces the existing problems and displays better image quality.

An enhanced Convolutional Neural Network (CNN) model for the detection of lung cancer using X-Ray image

Lung Cancer is a life-threatening disease which can be diagnosed by Medical Imaging such as X-Ray, MRI, CT Scan etc. This research presented an enhanced model using Convolutional Neural Network (CNN) to detect lung cancer using X-Ray image. Medical image processing relies heavily on the diagnosis of lung cancer images. It aids doctors in determining the correct diagnosis and management. For many patients, lung cancer ranks among the mostdeadly diseases. Many lives can be saved if cancerous growth is diagnosed early. The purported model was predominantly built on Convolutional Neural Network (CNN) architecture and the model was built with enhanced features such as Image Enhancement, Segmenting ROI (Region of Interest), Features Extraction and Nodule Classification. In preprocessing stage, the AMF (Adaptive Median Filter) filtering method was applied to eliminate noise in X-Ray image of the dataset, and quality of X-Ray image was improved with the support of CLAHE (Contrast Limited Adaptive Histogram Equalization). Secondly, K-means Clustering algorithm was used to extract the relevant feature or Region of Interest (ROI) of the lung field automatically i.e. the model was effectively trained to identify and crop the exact location of the lung field automatically. The model was able to classify the cancer nodule as either Cancerous or Non-Cancerous. The framework worked on C# platform, and used EMGU for detection of the tumour in the lung xray image. Experimental result showed that the developed system was able to detect Lung Cancer with 90.77% accuracy, 86.65% precision and 95.31% Recall/Sensitivity.