Adaptive Histogram Equalization Research Articles

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

An Evaluation of Fuzzy in Image Enhancement: Design and Comparison for Penicillium and Aspergillus Species

The main focus in this study is to enhance and classify the image of a type of filamentous fungi named Penicillium and Aspergillus. For image enhancement, fuzzy-partition gamma adaptive histogram equalization (FpGAHE) is proposed to improve the quality of an image, in particular the low quality of a microscopic image. Two stages have been considered in this technique. In the first stage, a fuzzy partition is developed to handle the inconsistency of the grey level values of the images by introducing a fuzzy set. In the second stage, surrounding neighbourhood is employed to avoid the imbalance data and reduce the drastically changes of brightness values of the image. The performances are evaluated into two parts i.e., image processing and image classification by using the collected microscopic images of fungi species. To evaluate the effectiveness of the proposed technique, the existing techniques, HE, AHE, CLAHE, GC and AGC is compared to the FpGAHE. In image processing, the result attained shows that the proposed technique has a better performance by obtaining the highest value for the PSNR, SSIM and FSIM evaluation for the species of A. terreus in clean condition. Meanwhile, in image classification, five different nearest neighbour classifiers have been tested. The results show the proposed FpGAHE with Improved Fuzzy-Based k Nearest Centroid Neighbour (IFkNCN) classifier perform the best result compare to other nearest neighbour classifier by obtaining the value of 92.59 and 93.95 for the salt and pepper and Gaussian noise corrupted images respectively.

Cascaded combination of total variation regularization and contrast limited adaptive histogram equalization based image dehazing

ABSTRACT Haze and fog can significantly reduce the visibility of distant objects by scattering light and creating blurry, washed-out images lacking in detail. Image dehazing is a technique used in computer vision to enhance the clarity of such obscured images. This study examines the effectiveness of various dehazing methods using real hazy images and synthetic images with artificially created haze. Performance metrics such as PSNR, SSIM, MSE, CII, and computation time are used to evaluate the proposed method. The evaluation is carried out on datasets like RESIDE, GMAN, O-Haze, I-Haze, NH-Haze, and Dense haze to compare the proposed method with existing models. The proposed method attains 27.46%, 20.63% and 21.09% higher PSNR and 12.36%, 23.95% and 36.12% lower Natural Image Quality Evaluator for RESIDE dataset when analyzed to the existing models, such as strategic method towards contrast enhancement by 2D histogram equalization under TV decomposition (CE-TDHE-TVD), multiple level framework basis contrast enhancement for uniform with non-uniform back ground imageries utilizing appropriate histogram equalization (CE-VHE), and Contrast enhancement with brightness preservation of low light pictures with the help of combined CLAHE and BPDHE histogram equalization (CE-CLAHE-BPDHE) respectively.

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

Background: Glioma is the most common brain tumor in adult patients and requires accurate treatment. The delineation of tumor boundaries must be accurate and precise, which is crucial for treatment planning. Currently, delineating boundaries for tumors is a tedious, time-consuming task and may be prone to human error among oncologists. Therefore, artificial intelligence plays a vital role in reducing these problems. Objective: This study aims to find a relationship between improving image enhancement and evaluating the performance of deep learning models for segmenting glioma image data on brain MRI images. Materials and methods: The BraTs2023 dataset was used in this study. The image dataset was converted from three dimensions to two dimensions and then subjected to pre-processing via four image enhancement techniques, including contrast-limited adaptive histogram equalization (CLAHE), gamma correction (GC), non-local mean filter (NLMF), and median and Wiener filter (MWF). Subsequently, it was evaluated for structural similarity index (SSIM) and mean squared error. The deep learning segmentation model was created using the U-Net architecture and assessed for dice similarity coefficient (DSC), accuracy, precision, recall, F1-score, and Jaccard index for tumor segmentation. Results: The performance of enhanced image results for CLAHE, GC, NLMF, and MWF techniques shows SSIM values of 0.912, 0.905, 0.999, and 0.911, respectively. The dice similarity coefficient (DSC) for segmentation without image enhancement was 0.817. The DSC of segmentation for CLAHE, GC, NLMF, and MWF techniques were 0.818, 0.812, 0.820, and 0.797, respectively. Conclusion: The enhanced image technique could affect the performance of tumor segmentation. by the enhanced image for use in a trained model may increase or decrease performance depending on the chosen image enhancement technique and the parameters determined by each method.

REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

One's face is a physical depiction of who they are. As a result, we have suggested a facial recognition- based automatic student attendance system. The usage of face recognition technology in everyday life is particularly beneficial for security control systems. Face recognition is used by the FBI (Federal Bureau of Investigation) for criminal investigations as well as the airport security system to identify criminals. In the first step of our suggested method, video framing, the camera is activated using a user-friendly interface. The Viola-Jones method is used to identify and segment the face ROI from the video frame. If necessary, image size scaling is done at the pre-processing step to prevent information loss. After using the median filter to reduce noise, color pictures are transformed into grayscale versions. Then, to improve the contrast of the pictures, contrast-limited adaptive histogram equalization (CLAHE) is applied. Principal component analysis (PCA) and improved local binary pattern (LBP) are used appropriately in the face identification stage to extract the characteristics from facial pictures. By minimizing the lighting impact and boosting identification rates, the improved local binary pattern outperforms the original local binary pattern in our suggested method. The features that were retrieved from the training pictures and the features that were extracted from the test images are then compared. The face pictures are then identified and categorized using the upgraded LBP, PCA, and algorithm that produced the best results. The acknowledged student's attendance will then be recorded and saved in the excel file. A warning will be sent if a student signs in more than once. The student who is not enrolled will also be allowed to register right away. When two photographs per person are trained, the average recognition accuracy is 100% for high-quality images, 94.12% for low-quality images, and 95.76% for the face database.

Adaptive histogram equalization with visual perception consistency

Histogram equalization (HE) is a well-established method for image contrast enhancement due to its simplicity and effectiveness. However, it suffers from three main shortcomings, i.e., over-enhancement, under-enhancement and mean shift. To address these issues, this paper proposes a systematic scheme, that is, adaptive histogram equalization with visual perception consistency (AHEVPC). Firstly, a novel histogram correction model is designed to get the optimal controlling parameters, which specifically address the aforementioned issues of HE. Besides, considering the subjective perception of the initial output of the model, two strategies are proposed to make the enhanced image more natural and comfortable. Finally, histogram equalization is applied to the modified histogram. Extensive experimental results demonstrate that the proposed scheme is reasonable and effective, and outperforms several state-of-the-art methods in terms of subjective and objective metrics.

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification

Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.

MINM: Marine Intelligent Netting Monitoring Using Multi-Scattering Model and Multi-Space Transformation

Marine intelligent net tank aquaculture monitoring plays an important role in improving aquaculture efficiency, environmental monitoring efficiency, and environmental safety. The underwater environment has complex light, often with problems such as scattering and absorption, resulting in poor image quality, making it difficult to accurately analyze and judge the aquaculture environment. Improving marine intelligent net tank aquaculture monitoring has the following three advantages: 1) better observation and monitoring of the aquaculture process, timely detection of problems and abnormalities, to protect the benefits of aquaculture and product quality. 2) more convenient and rapid monitoring of the aquaculture environment, improving monitoring efficiency and reducing monitoring costs. 3) effective monitoring of the underwater environment around the farm, and timely detection of foreign pollution, harmful substances, and other problems, to protect the safety of the aquaculture environment. Therefore, in order to solve the two degradation problems of scattering and absorption in the process of marine smart net farm monitoring, we propose a marine smart net farm monitoring method using multiple scattering models and multiple spatial transformations, called MINM. Specifically, inspired by the image chromatic aberration correction method, we design a color correction method in the multicolor space, which is implemented by using the Lab and RGB color space by performing contrast-constrained adaptive histogram equalization and gray world assumptions, respectively, to correct color shifts in different color models. Based on this, we propose a de-scattering method using a multi-scattering model, which eliminates the effect of scattering on underwater imaging by embedding a complete multi-scattering underwater imaging model to guide the extraction of different features in the multi-scattering model. To obtain more qualified results, we also propose an efficient perceptual fusion to mix the output of the de-scattering and color correction. Thus, our method can take advantage of multiple scattering models and multiple spatial transformations to effectively improve the visual quality of underwater images, producing enhanced results that fit the complete underwater imaging model and have bio-visual characteristics. In extensive experimental demonstrations, our MINM method has shown higher performance than the state-of-the-art methods in terms of both visual quality and quantitative metrics. All experimental results and datasets in this paper are available from the following website: https://github.com/An-Shunmin/MINM.

Enhancing visual clarity in rainy conditions based on single-frame filtering algorithm

Vision-based driving assistance systems has made a paradigm shift in the automotive industry, especially in auto electronics, towards vehicular technology in smart cities. This technology concentrates on safeguarding the members inside and outside the car, such as pedestrians or other vehicles on the road. This paper proposes a methodology that employs a single image/frame-based rain filter for video processing. In this paper, an innovative method of frame filtering is statistically modelled to provide a clear vision on the road during rainy weather. The frame filtering displays an improvement by considering a reference frame as the input frame and it is further exploited to confiscate the contextual and atmospheric particles using the method of frame difference. Moreover, this article suggests a hybrid algorithm that accomplishes filtering utilizing L0-gradient image smoothing and weights least square smoothing along with adaptive histogram equalization to preserve and enhance the image under intense rainy conditions. With a single frame-based filtering methodology, the proposed algorithm performs well with rainy images but loses information during video processing. The proposed method displays an improvement by 36.29 % and 3.48 % in Structural Similarity Index (SSIM) and 57.15 % and 14.47 % in Gradient Magnitude Similarity Deviation (GMSD) as compared to guided filter and bilateral filter methods.

Ad-Tl: Alzheimer’s Disease Prediction Using Transfer Learning

Although many statistical methods and machine learning algorithms have been explored in both clinical and research settings to extract these patterns from neuroimaging data, differentiating between Alzheimer's disease and healthy brain data in older adults (age > 75) has proven challenging due to highly similar patterns of brain atrophy and image intensities. Medical image analysis is just one field that has benefited from the widespread use of deep learning technologies in recent years. This research paper proposed AD prediction using transfer learning (AD-TL) methods. The MRI dataset has been normalized using the Multi-Layer Perception model (MLP) with the CNN algorithm. In order to improve images, the CLAHE (Contrast-Limited Adaptive Histogram Equalization) technique has been used. Image segmentation has been done with Watershed Image segmentation. The training has been done with the Residual network (ResNet 50) with Alex net. Finally, the classification has been done with the Deep Convolutional neural network (DCNN) algorithm.According to the experimental data, the classification accuracy of the technique provided in this study may reach 99%.

Design of Novel Brain Tumor Segmentation System Using Hybrid Heuristic-Aided Multiscale Self-Guided Attention Mechanism-Based Adaptive Unet+++ with 3D Brain MRI Images

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3+ (MSGAM-AUNet3+), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques.

Diabetic retinopathy prediction based on vision transformer and modified capsule network

Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it increases as long as a person suffers from diabetes. Protecting the sight of the patient or decelerating the evolution of this disease depends on its early detection as well as identifying the exact levels of this pathology, which is done manually by ophthalmologists. This manual process is very consuming in terms of the time and experience of an expert ophthalmologist, which makes developing an automated method to aid in the diagnosis of diabetic retinopathy an essential and urgent need. In this paper, we aim to propose a new hybrid deep learning method based on a fine-tuning vision transformer and a modified capsule network for automatic diabetic retinopathy severity level prediction. The proposed approach consists of a new range of computer vision operations, including the power law transformation technique and the contrast-limiting adaptive histogram equalization technique in the preprocessing step. While the classification step builds up on a fine-tuning vision transformer, a modified capsule network, and a classification model combined with a classification model, The effectiveness of our approach was evaluated using four datasets, including the APTOS, Messidor-2, DDR, and EyePACS datasets, for the task of severity levels of diabetic retinopathy. We have attained excellent test accuracy scores on the four datasets, respectively: 88.18%, 87.78%, 80.36%, and 78.64%. Comparing our results with the state-of-the-art, we reached a better performance.

An integrated enhancement method to improve image visibility and remove color cast for sand-dust image

AbstractSand-dust color images suffer from poor image visibility and serious color cast that significantly affect the performance of outdoor computer vision systems. Therefore, this paper proposes an integrated enhancement method for the sand-dust image. The proposed method improves the image visibility and removes the sand-dust color cast. It integrates two main processes in two different color models. The adaptive gray world-blue channel (AGW-B) is utilized in the Red-Green-Blue (RGB) color model to remove the sand-dust color cast. Then, the contrast limited adaptive histogram equalization with normalized intensity and saturation correction (CLAHE-NISC) is conducted in a Hue-Saturation-Intensity (HSI) color model to enhance the image visibility. Sand-dust images with weak, medium, and extreme sand-dust color casts were utilized in the subjective and objective evaluations. Results show that the proposed method produced better and clearer enhanced images than the other four current sand-dust image enhancement methods.

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

High-Temperature Materials Science Experiment Cabinet on the Chinese Space Station is mainly used to carry out experimental research related to high-temperature materials science in microgravity. It is equipped with an X-ray transmission imaging module, which is applied to realize transmission imaging of material samples under microgravity. However, the X-ray light source is far away from the experimental samples, and the images obtained by the module are blurred, so it is impossible to accurately observe the morphological changes during the melting and solidification processes of high-temperature materials. To address this issue, this paper proposed a contrast enhancement algorithm specifically designed for X-ray images obtained during the experiments of high-temperature materials. The algorithm is based on gradient three-interval equalization, and it is combined with a Gaussian function to expand the gradient histogram. Meanwhile, the local gray level information within each gradient interval is corrected by designing an improved adaptive contrast enhancement algorithm. By comparing with Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms, EnlightenGAN, and Wavelet algorithms, the Contrast Enhancement based contrast-changed Image Quality measure (CEIQ) and Measure of Enhancement (EME) are improved by an average of 56.97%, 10.58%, and Measure of Entropy (MOE) are improved by an average of 7.74 times. The experimental results show that the algorithm makes the image details clearer on the basis of image contrast enhancement. The solid-liquid interface in the image can be clearly observed after contrast enhancement. The algorithm provides strong support for the study of interface dynamics during the experiment process of high-temperature materials.

Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning

This study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non-patient controls, with eye images and biodemographic data collected at a Cardiology outpatient clinic. The study explored three scenarios: Scenario-I focused on biodemographic data, Scenario-II on iris features, and Scenario-III combined iris images and data. Iris processing included location determination, normalization, and heart region selection, with image enhancement via adaptive histogram equalization. Feature extraction through a 2-level wavelet transform generated 272 attributes, including statistical, Gray Level Co-occurrence Matrix, and Gray Level Run Length Matrix features for eight subcomponents. Correlation-based selection identified the best features, and classification employed ML techniques and incorporated stacking learning to enhance the results. Scenario-I achieved the highest accuracy at 83.57% among all evaluated algorithms. In Scenario-II, the proposed algorithm consistently outperformed others, achieving 94.88% accuracy and strong performance in other metrics, highlighting its effectiveness. In Scenario-III, the algorithm maintained superiority with 96.07% accuracy, specificity, recall, and area under the curve values. The proposed algorithm consistently outperforms other methods across scenarios, indicating its potential for CAD diagnosis, making it a promising choice for future CAD systems. The proposed algorithm presents a novel approach to the preliminary diagnosis of CAD, eliminating the necessity for electrocardiography, echocardiography, or effort tests. It also enables seamless integration into telemedicine systems, allowing for tele-diagnosis to conduct preliminary assessments before routine clinical practice.

Design of an Iterative Method for Enhanced Retinal Image Analysis Using Stacked Deep Learning Operations

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.

Lung computed tomography image enhancement using U‐Net segmentation

AbstractThe goal of image enhancement methods is to improve image's quality. The efficacy of U‐net is evident through its extensive utilization across various significant image modalities, involving computed tomography (CT) scans, magnetic resonance imaging, X‐rays, and microscopy. In this study, we provided a novel and efficient strategy to improve lung CT images based on segmentation using U‐Net architecture. Subsequently, contrast enhancement was performed using adaptive histogram equalization and dark channel prior methods. Finally, the lightness of the lung CT image was enhanced using nonlinear mapping. The contrast enhancement performance of the suggested method is quantified by various measures like the average gradient, mean of the local standard deviation, contrast enhancement measure, and structural similarity index. The performance of the suggested method is compared against other methods, and the results indicate that the suggested method achieves better quality measures of 23.4907, 55.20341, 0.961674, and 0.4143 for the four performance metrics.

Deep Learning Enabled Efficient Net with KLM for Pragmatic Plant Disease Diagnosis

Research is still being done on the automated identification and classification of plant diseases. A quick and precise methodology for identifying plant diseases can improve both small-scale commercial crop protection and large-scale food security. Deep learning (DL) methods also enable the monitoring of plant health and the early diagnosis of illnesses. This study's DLEN-KLM model, which is built on EfficientNet and Kernel extreme Learning Machine (KLM) and is designed to diagnose plant diseases intelligently, addresses this issue. In contrast to limited adaptive histogram equalisation as a preprocessing method, the suggested DLEN-KLM model uses median filtering in its construction. A feature extractor built on EfficientNet B0 is also part of the DLEN-KLM model, which is used to create the best feature vectors before categorising them with the KLM model. Utilising a benchmark dataset, the effectiveness of the DLEN-KLM technique is validated. The experimental results showed that the technique outperformed more contemporary methods in terms of disease diagnosis.

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN vs VGG Architecture on CLAHE- Preprocessed Retinal Images

Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images.   

Segnet with Unet3+ and EfficientNet: a novel framework of brain tumour segmentation and classification model by multiscale attention-based deep learning techniques with hybrid heuristic improvement using 3D MRI brain images

ABSTRACT An adaptive deep learning is recommended to segment and classify the brain tumor using 3D MRI images. Initially, the original 3D MRI images are gathered and fed into pre-processing, which is accomplished using the 'Contrast Limited Adaptive Histogram Equalization (CLAHE)' technique. The pre-processed image is given as input to 'Multiscale Self-guided Attention based Hybrid Adaptive Networks (MSA-HAN)', where it encompasses Segnet with Unet3+ for segmentation. In this model, certain parameters are optimized using the Position Updating of Black Widow and Shark Smell (PUBWSS). Finally, the segmented region is subjected into the Adaptive Atrous Spatial Pyramid Pooling (ASPP) based EfficientNet (AA-ENet) that considers MobileNet and DenseNet. Thus, the model's performance is evaluated, and results are carried out with diverse measures. The performance of the offered method shows 95.74%, and 95.80% in terms of accuracy and F1-score. Hence, the results declare that the developed model achieves higher segmentation accuracy than other approaches.