Contrast Limited Adaptive Histogram Equalization Research Articles

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.

Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data.

The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models. This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images. For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique. The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.

Early Breast Cancer Detection Based on Locally Available Mammography Data Using Convolutional Neural Network

In this work, convolutional neural network model was developed to read mammography image scans to predict malignancy. Firstly, mammography image data was sourced locally from University of Abuja Teaching hospital, Gwagwalada, Nigeria and then combined with publicly available mammography image dataset from The Mammographic Image Analysis Society (MIAS) database. The data obtained were then preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE), Image Denoising, Padding and Formatting. Transfer learning was implemented by re-architecting pre-trained VGG-16, VGG-19, MobileNet-V2 and DenseNet-121 models to have an output layer with two neurons and softmax activation function, each signifying the degree to which calcifications in the mammography image is benign or malignant. The model was then trained on the preprocessed data and evaluated, the following metrics were achieved: VGG-16 and VGG-19 based models achieved an accuracy of 86% each, precision of 87% and 88% respectively and recall of 92% and 93% respectively. MobileNet-V2 has accuracy of 90%, precision and recall of 93% while the DenseNet-121 model achieved an accuracy of 95%, precision of 93% and 100% score in recall. MobileNet-V2 performed best in the computational complexity analysis with 336.34 MFLOPs, followed by DenseNet-121 with 5.69GFLOPs. VGG-16 and VGG-19 have computational complexity of 15.3 GFLOPs and 19.6 GFLOPs respectively.

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation.

Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. Our model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE). The evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach. Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

Research on marine flexible biological target detection based on improved YOLOv8 algorithm.

To address the challenge of suboptimal object detection outcomes stemming from the deformability of marine flexible biological entities, this study introduces an algorithm tailored for detecting marine flexible biological targets. Initially, we compiled a dataset comprising marine flexible biological subjects and developed a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, supplemented with a boundary detection enhancement module, to refine underwater image quality and accentuate the distinction between the images' foregrounds and backgrounds. This enhancement mitigates the issue of foreground-background similarity encountered in detecting marine flexible biological entities. Moreover, the proposed adaptation incorporates a Deformable Convolutional Network (DCN) network module in lieu of the C2f module within the YOLOv8n algorithm framework, thereby augmenting the model's proficiency in capturing geometric transformations and concentrating on pivotal areas. The Neck network module is enhanced with the RepBi-PAN architecture, bolstering its capability to amalgamate and emphasize essential characteristics of flexible biological targets. To advance the model's feature information processing efficiency, we integrated the SimAM attention mechanism. Finally, to diminish the adverse effects of inferior-quality labels within the dataset, we advocate the use of WIoU (Wise-IoU) as a bounding box loss function, which serves to refine the anchor boxes' quality assessment. Simulation experiments show that, in comparison to the conventional YOLOv8n algorithm, our method markedly elevates the precision of marine flexible biological target detection.

Efficient-VGG16: A Novel Ensemble Method for the Classification of COVID-19 X-ray Images in Contrast to Machine and Transfer Learning

Corona Virus Disease 2019 (COVID-19) is a contagious respiratory disease characterized by its high transmissibility and exponential spread, presenting persistent difficulties. This investigation sought to incorporate machine learning (ML) and transfer learning (TL) and introduced Efficient-VGG16, a novel ensemble approach that integrated the EfficientNet-B0 and VGG16 architectures to classify chest X-ray images as either COVID-19 positive or non-COVID. The COVID-Xray-5k dataset used in the research was split into different training and testing sets (with ratios of 80:20, 75:25, and 70:30), preprocessed, and then used to evaluate the approach’s effectiveness. Throughout the investigation, all procedures operated effectively at the same selected image size, 224*224. Preprocessing included Gaussian blur to remove noise and relics, while Contrast Limited Adaptive Histogram Equalization (CLAHE) adjusted contrast. Among the employed ML classifiers, XgBoost attained the highest accuracy, with F1 scores of 95.78% and 71.56%, respectively. In addition, the research investigated the effectiveness of TL using pre-trained convolutional neural networks (CNNs) such as VGG16, MobileNetV3, ResNet101, Densenet201, and EfficientNet-B0. EfficientNet-B0 achieved the highest levels of accuracy and F1 score, 99.31% and 93.72%, respectively. Furthermore, the proposed Efficient-VGG16 achieved accuracy and an F1 score of 99.46% and 98.41%, respectively, and outperformed TL with EfficientNet-B0 and XgBoost. The investigation demonstrated the effectiveness of the Efficient-VGG16 for accurately classifying COVID-19.

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.

Enhanced Breast Cancer Detection Using Modified Particle Swarm Optimization-Based Back Propagation Neural Network

Breast cancer detection faces challenges due to late diagnosis and inaccurate results, leading to improper treatment. Particle Swarm Optimization (PSO), a common optimization technique, often gets trapped in local optima, resulting in high false negatives and computational time. To address this, a study devised a modified PSO-based Back Propagation Neural Network (BPNN) using White Shark Optimization (WSO) for more accurate breast cancer detection. The research collected 1097 mammogram images from two medical centers and categorized them into benign, and malignant groups. Image preprocessing involved techniques like Otsu's method and contrast limited adaptive histogram equalization, with Fuzzy C Means used for segmentation. Region of Interest (ROI) extraction utilized Gabor filter and Local Binary Pattern, followed by weighted average fusion of textural features. WSPSO was then applied for optimal feature selection, and BPNN classified the identified features. MATLAB R2016a implemented the system. Evaluation metrics included accuracy, precision, sensitivity, specificity, False Positive Rate (FPR), and Computation Time (CT). Compared to PSO-BPNN, WSPSO-BPNN demonstrated superior performance. The accuracy, precision, sensitivity, specificity, FPR and CT of WSPSO-BPNN for malignant dataset were 96.88%, 99.08%, 96.43%, 97.92%, 2.08%, and 32.38 seconds, respectively, while the corresponding values for PSO-BPNN were 93.13%, 96.33%, 93.75%, 91.67%, 8.33% and 44.03 seconds, respectively. Also for the accuracy, precision, sensitivity, specificity, FPR and CT of WSPSO-BPNN for benign dataset were 96.15%, 98.14%, 96.34%, 95.71%, 4.29%, and 40.04 seconds, respectively, while the corresponding values for PSO-BPNN were 93.59%, 96.27%, 94.51%, 91.43%, 8.57% and 61.94 seconds respectively showcasing its potential for improving breast cancer diagnosis. Keywords: Back Propagation Neural Network, Breast Cancer, Classification, Mammogram Images, Particle Swarm Optimization, White Shark Optimization. Oyerinde, I.M., Olabiyisi, S.O., Falohun, A.A. & Awodoye, O.O. (2024); Enhanced Breast Cancer Detection Using Modified Particle Swarm Optimization-Based Back Propagation Neural Network. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 53-62. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P5

Enhancing breast cancer detection from histopathology images: A novel ensemble approach with deep learning-based feature extraction

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.

Target detection and classification via EfficientDet and CNN over unmanned aerial vehicles.

Advanced traffic monitoring systems face significant challenges in vehicle detection and classification. Conventional methods often require substantial computational resources and struggle to adapt to diverse data collection methods. This research introduces an innovative technique for classifying and recognizing vehicles in aerial image sequences. The proposed model encompasses several phases, starting with image enhancement through noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Following this, contour-based segmentation and Fuzzy C-means segmentation (FCM) are applied to identify foreground objects. Vehicle detection and identification are performed using EfficientDet. For feature extraction, Accelerated KAZE (AKAZE), Oriented FAST and Rotated BRIEF (ORB), and Scale Invariant Feature Transform (SIFT) are utilized. Object classification is achieved through a Convolutional Neural Network (CNN) and ResNet Residual Network. The proposed method demonstrates improved performance over previous approaches. Experiments on datasets including Vehicle Aerial Imagery from a Drone (VAID) and Unmanned Aerial Vehicle Intruder Dataset (UAVID) reveal that the model achieves an accuracy of 96.6% on UAVID and 97% on VAID. The results indicate that the proposed model significantly enhances vehicle detection and classification in aerial images, surpassing existing methods and offering notable improvements for traffic monitoring systems.

Atelectasis detection in chest X-ray images using convolutional neural networks and transfer learning with anisotropic diffusion filter

Atelectasis is the loss of volume caused by decreasing gas in a specific area of the lung. It occurs when the lung sacs (alveoli) do not fully inflate, resulting in a lack of oxygen to the blood, tissues, organs, and fill with alveolar fluid. It can be caused by pressure outside of your lung, an obstruction, poor airflow, or scarring. It is critical to diagnose this lung condition as soon as possible. Chest X-rays are the most commonly utilized diagnostic tool for this condition. Examining chest X-rays, however, is difficult even for a professional radiologist. There is a need to improve diagnosis accuracy. As a result, this study proposes a novel detection and classification approach for rapid diagnosis of atelectasis utilizing patient chest X-ray data. To diagnose atelectasis from chest X-ray images, we used state-of-the-art models like VGG19, Inception, and a deep learning method (CNN). This study presents an effective method for categorizing chest X-ray images as normal or atelectasis-infected. This study offers a convolutional neural network (CNN) method to aid medical experts in identifying atelectasis disease. The anisotropic diffusion filtering (ADF) approach was used to improve image edge preservation, reduce noise, and contrast limited adaptive histogram equalization (CLAHE) for improving the contrast of low-intensity images. After evaluating the CNN model, it achieved 99.88 % training accuracy, 99 % validation accuracy, and 99 % test accuracy. In this study, CNN achieved a result that outperformed state-of-the-art models (VGG19 and Inception). As a result of the findings, deep features provided consistent and reliable features for detecting atelectasis. Therefore, the suggested method expedites atelectasis diagnosis and radiologists' screening of atelectasis patients.

A convolution neural network for rapid and accurate staging of breast cancer based on mammography

IntroductionBreast cancer (BC) has been one of the main reasons for women's deaths in the recent decade. This study hypothesizes that the deep features resulting from mammography staging can be used to determine tumor staging and lymph nodes of the sentinel lymph node (SLN) before surgery in BC patients. MethodsA retrospective study of female BC patients being treated. Pathological variables were collected from medical records, including age, tumor location, staging based on the American Joint Committee on Cancer (AJCC), pathological type, human epidermal growth factor receptor 2 (HER2) status, Progesterone Receptor (PR) status, and Estrogen Receptor (ER) status. The contrast enhancement technique with Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to the studied data using MATLAB software. The images were first changed from color to gray in the preprocessing process, and then the images were changed from gray to color again. The regions of interest (ROI) of the primary tumor were isolated from other areas of breast tissue by two expert radiologists. Therefore, a proposed convolution neural network (CNN) model was applied. ResultsIn this study, 390 patients' information was used to analyze the data. In total, out of 390 patients, 300 had metastatic SLN involvement, and 390 had a tumor size greater than 1 mm. Performance evaluation of the proposed CNN staging was AUC 0.919 (95 % CI: 0.852,0.971) for the training group and AUC 0.877 (95 % CI: 0.805,0.949) for the validation group. ConclusionsThis study proposes a deep learning model based on CNN based on mammographic images, which has a suitable function for determining tumor staging and SLN metastasis.

Hybrid Simulated Annealing‐Evaporation Rate‐Based Water Cycle Algorithm Application for Medical Image Enhancement

Visualizing medical images is difficult due to artifacts, poor local contrast, low soft tissue contrast, excessive noise levels, and a wide dynamic range. This has created a serious problem for physicians, resulting in unapproachable and inaccurate disease diagnoses. To circumvent this problem, this research proposes a medical image enhancement (MIE) approach based on the hybrid simulated annealing‐evaporation rate‐based water cycle algorithm (SA‐ERWCA). The ERWCA enhances distorted medical images by finding the best optimal solution for the transformation parameters according to the fitness function. The fitness function consists of three objective measurements, namely, entropy, number of edges, and sum of edge intensities. Due to its high potential for finding a globally optimal solution, SA is applied solely to determine the optimized initial population of the ERWCA, thereby enhancing its convergence characteristics. To simply put, the main objective of SA is to avoid premature convergence of the ERWCA. Thus, blending SA and ERWCA produces better‐quality medical images. The performance of the proposed algorithm was compared to histogram equalization (HE), low contrast stretching (LCS), contrast‐limited adaptive histogram equalization (CLAHE), particle swarm optimization (PSO), accelerated PSO (APSO), and the water cycle algorithm (WCA). Along with the objective function fitness, seven full reference (FR) image quality assessment (IQA) metrics were implemented to evaluate image quality and compare performance. The findings of the present study showed that the proposed strategy outperforms all of the compared methods in terms of objective function fitness and perceptual visual IQA metrics such as the Harr wavelet‐based perceptual similarity index (HaarPSI), visual signal‐to‐noise ratio (VSNR), and information content weighted structural similarity index measure (IW‐SSIM). The suggested technique also exhibited better stability and faster convergence time to the optimum solution. Furthermore, the proposed approach outperformed others by avoiding premature convergence to the best solution and providing excellent optimization results with acceptable computational efficiency.

Multi-disease Classification of Mango Tree Using Meta-heuristic-based Weighted Feature Selection and LSTM Model

Global food security can be influenced by the diseases in crop plants as several diseases straightforwardly influence the quality of the grains, vegetables, fruits, etc., which also results in affecting of agricultural productivity. Like other plants, the mango tree is also affected by several diseases, and also the identification of multi-disease classification with a single leaf is more complex, and also it is impossible to detect diseases with bare eyes. Based on the other plants, the mango tree is also affected by various diseases, which is more difficult to detect the disorders with bare eyes. It is error-prone, inconsistent, and unreliable. Here, the mango trees are affected during the production, and also affect the plant health regarding multi-diseases. When the plants are affected by the diseases, it may cause fewer amounts of productivity, as a result, impacting the economy. However, it is more critical to detect plant diseases with the large varieties of trees and plants. Various research tasks on deep learning approaches focus on identifying the diseases in plants including leaves and fruits. Thus, the main objective of this paper is to implement an effective and appropriate technique for diagnosing mango tree diseases and their symptoms through fruit and leaf images, and thus, there is a need for an appropriate system for cost-effective and early solutions to this problem. Hence, the main intention of this work is to implement an efficient and suitable technique for diagnosing mango tree diseases and also identify the symptoms through fruit and leaf images. Intending to overcome the existing challenges, there is a need for an appropriate system for achieving cost-effectiveness and also creating an early solution to resolve this problem. This paper intends to present novel deep learning models for mango tree multi-disease classification. Initially, the data collection is done for gathering the diseased parts of the mango tree in terms of leaf and fruit images. Then, the contrast enhancement of the images is performed by the “Contrast-Limited Adaptive Histogram Equalization (CLAHE)”. For the deep feature extraction of leaf images, and fruit images, Convolutional Neural Network (CNN) is employed, and the features from both inputs are concatenated for further processing. Further, the weighted feature selection is adopted for selecting the most significant features by the Adaptive Squirrel-Grey Wolf Search Optimization (AS-GWSO). Enhanced “Long Short Term Memory (LSTM)” is applied in the classification part with parameter optimization using the same AS-GWSO for enhancing classification accuracy. At last, the results of the designed system on various mango tree diseases verify that the designed approach has yielded the highest accuracy by evaluating conventional approaches. Therefore, it would also alleviate and treat the affected mango leaf diseases accurately.

Deep Learning Techniques in Classification of Stages in Dementia: An Ensemble Approach

Dementia is the severe brain disease that affects the memory system of the humans which requires early diagnosis. It progresses through different stages from very mild dementia, which motivates to find the stage of dementia for treating the affected persons. A novel ensemble-based classification model to classify the stages in dementia such as mild-dementia, non-dementia, very-mild dementia has been proposed in this work. The MRI images were pre-processed with techniques such as resizing, data augmentation and Contrast Limited Adaptive Histogram Equalization (CLAHE) and different four forms of datasets such as Image Data-1, Image Data-2, Image Data-3, Image Data-4 are framed to justify preprocessing techniques. The pre-trained models namely ResNet, Inception, MobileNet, VGG, DenseNet, Xception, Inception Resnet, AlexNet, EfficientNet and NasNet Mobile are trained with all four forms of dataset and performance of each model is analysed. The models DenseNet201, MobileNetV2, VGG19, ResNet152 achieved accuracy of 93%,93%,93%.90% respectively with Image Data-4. The proposed model is the ensemble of DenseNet201, MobileNetV2, VGG19, ResNet152 which provides accuracy precision, recall and f1-score of 95%, 93%, 100% and 96% respectively. This ensemble model has undergone testing using a brain tumour dataset, and the experimental findings indicate that the proposed framework enhances the classifier's generalization capabilities for multiclass problems.

SiamFCA: A new fish single object tracking method based on siamese network with coordinate attention in aquaculture

In aquaculture, tracking fish helps to obtain information about their growth, behavior, location and health, etc. However, there are some problems in the process of fish tracking. Firstly, to solve the noise problem of underwater images, we use the contrast limited adaptive histogram equalization (CLAHE) to carry out local histogram equalization in each small block, which increases the image contrast while retaining more details. This operation enhances the contrast of the image while suppressing noise to solve the problem of noisy images. Then, to solve the problems of similarity interference, occlusion and scale variation among fish, we propose a new method for fish single object tracking in aquaculture environments, called SiamFCA, by combining the coordinated attention mechanism (CA). The method is based on the siamese network framework and uses a modified AlexNet to extract the depth features of image pairs. And through the coordinate attention mechanism, the model can learn the relationship between different objects more accurately, which can improve the accuracy and robustness of the model when dealing with complex image scenes. Finally, the correlation operation between classification and regression is performed based on the region proposal network (RPN). By classifying, region suggestions can be labeled as positive or negative samples, and regression can calibrate the position and size of the region proposal to match targets more accurately. The experimental results show that the precision and success rate of the algorithm are 84.2 % and 62.4 %, respectively, which are better than the tracking methods such as SiamRPN and DaSiamRPN, etc. The SiamFCA runs at 83 fps, which meets the demand of real-time fish tracking in aquaculture.

Lung cancer detection via deep learning-based pyramid network with honey badger algorithm

Lung carcinoma is a fatal illness characterized by the unchecked growth of tumors in the respiratory tract. A diagnosis of cancers and their dimensions and forms are often difficult to ascertain from imaging tests because of their irregular form. And high discrepancy in health images is a severe problem in health image investigation. To overcome this issue a novel deep learning-based lung cancer detection (DL-LCD) model is proposed. The LIDC-IDRI and NIH Chest X-ray dataset was used which comprises CT scan and CXR images. Initially, these images are pre-processed using CLAHE (Contrast Limited Adaptive Histogram Equalization) by eliminating noise distortions and enhancing the image clarity. The lung images are segmented using Honey Badger Algorithm. The segmented images are classified into cancerous and non-cancerous. Based on the LIDC-IDRI database, achieves 98.5 % accuracy in normal lungs and 97.44 % accuracy in abnormal lungs. The results of the experiments indicate that the proposed approach outperforms existing approaches.

Comparative analysis of paddy leaf diseases sensing with a hybrid convolutional neural network model

The major food in south India is Paddy. Early detection of leaf disease can increase the production of crops. In the agricultural field, farmers can control the spreading of leaf disease with the help of the modeling of automated disease classification. In comparison to the manual approach to paddy leaf infection detection via visual examination, automated identification of rice illnesses using an image of the paddy leaf may be advantageous. Deep learning, a highly well-liked and effective machine learning method, has recently demonstrated considerable promise in the categorization task of images. In this work, Contrast Limited Adaptive Histogram Equalization (CLAHE) pre-processes the augmented images of paddy leaves. The Gray level co-occurrence metrics (GLCM) model then extracts the features from an image of the pre-processed paddy leaves. The five disease classes of paddy leaves were finally identified using the hybrid CNN model as follows: Bacterial Leaf Blight (BLB), Sheath Rot (SR), Narrow Brown Leaf Spot (NBLS), Brown Spot (BS) and Leaf Smut (LS). MATLAB platforms handle implementation works. While compared to the previous approaches, the proposed model reveals the superiority of paddy leaves classification.

Analysis for diagnosis of pneumonia symptoms using chest X-ray based on MobileNetV2 models with image enhancement using white balance and contrast limited adaptive histogram equalization (CLAHE)

This study focuses on diagnosing pneumonia symptoms using chest X-ray (CXR) images. It employs the MobileNetV2 model alongside image enhancement techniques, including white balance and CLAHE. MobileNetV2 is a computationally efficient Convolutional Neural Network (CNN) known for its robust image recognition capabilities. White balance corrects color imbalances in CXR images, ensuring color consistency, while CLAHE enhances contrast and image details for improved analysis. The enhanced CXR images are fed into the pre-trained MobileNetV2 model, which distinguishes pneumonia and non-pneumonia cases. The study aims to enhance pneumonia diagnosis accuracy, benefiting from MobileNetV2’s efficiency and image enhancement. Notably, it achieved high accuracy and low loss for both three-class (91.17% accuracy, 35.0% loss) and two-class (99.76% accuracy, 7% loss) classifications, with the best results in the 50-epoch test. However, it is essential to consider the trade-off between computing time and the risk of overfitting when increasing epochs. Future research could explore additional features to further enhance model performance.

An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images

Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention. However, existing methods struggle to achieve a good balance between detection accuracy and efficiency. To address this issue, we propose a novel method for efficient PV cell defect detection. Firstly, we utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to improve EL image contrast, making defect features become more distinguishable. Secondly, we propose a lightweight defect detector using EfficientNet-B0 as its backbone. Moreover, we design a graph channel attention module (GCAM) to improve CNN’s limitation in modeling global information. It executes graph channel reasoning to generate enriched feature representation beyond the local receptive field, which is beneficial for distinguishing PV cell defects with similar local details. Next, we utilize focal loss to train the detector, enhancing its ability to detect challenging defects. Lastly, the proposed method is evaluated on the PVEL dataset and it achieved an accuracy of 97.81%, precision of 97.70%, recall of 97.59%, F1-score of 97.64%, and MCC of 97.32%, demonstrating our method is effective and outperforms state-of-the-art methods across various metrics.