Lung X-ray Images Research Articles

Tuberculosis Detection based on Lung X-Ray Images Using Convolutional Neural Networks (CNN)

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis, primarily affecting the lungs. Despite being preventable and curable, TB remains a significant global health issue, especially in developing countries. The success of TB treatment heavily depends on the accuracy of the diagnosis, which typically requires expertise from pulmonology or radiology specialists to interpret chest X-ray images. This study aims to design an assistive tool for TB detection that can automatically diagnose the disease using chest X-ray data. The study implemented a Convolutional Neural Network (CNN) architecture to analyze the X-ray images. Additionally, image preprocessing and early stopping methods were employed to enhance accuracy performance, optimize computation, and prevent overfitting. Experiment was conducting using 75% of the data as training data to generate the model and then applied to 25% of the data as testing data. This study comparing image sizes in RGB and grayscale modes. Experimental results show that the use of early stopping has a significant impact on training time, reducing training time substantially in almost all scenarios without drastically sacrificing accuracy. Without early stopping, accuracy does tend to be higher, as seen in grayscale color mode with an image size of 128x128, where the accuracy reaches 0.992, and in RGB mode with an image size of 64x64 which reaches 0.995. However, training time also increases significantly, for example for a 299x299 image with RGB mode, the training time reaches 927 seconds. Therefore, while RGB yields slightly higher accuracy, grayscale is recommended due to significantly faster training times. Additionally, the early stopping mechanism proves effective in reducing computational time, making the training process more efficient.

Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.

Since its introduction in 2015, the U-Net architecture used in Deep Learning has played a crucial role in medical imaging. Recognized for its ability to accurately discriminate small structures, the U-Net has received more than 2600 citations in academic literature, which motivated continuous enhancements to its architecture. In hospitals, chest radiography is the primary diagnostic method for pulmonary disorders, however, accurate lung segmentation in chest X-ray images remains a challenging task, primarily due to the significant variations in lung shapes and the presence of intense opacities caused by various diseases. This article introduces a new approach for the segmentation of lung X-ray images. Traditional max-pooling operations, commonly employed in conventional U-Net++ models, were replaced with the discrete wavelet transform (DWT), offering a more accurate down-sampling technique that potentially captures detailed features of lung structures. Additionally, we used attention gate (AG) mechanisms that enable the model to focus on specific regions in the input image, which improves the accuracy of the segmentation process. When compared with current techniques like Atrous Convolutions, Improved FCN, Improved SegNet, U-Net, and U-Net++, our method (U-Net++-DWT) showed remarkable efficacy, particularly on the Japanese Society of Radiological Technology dataset, achieving an accuracy of 99.1%, specificity of 98.9%, sensitivity of 97.8%, Dice Coefficient of 97.2%, and Jaccard Index of 96.3%. Its performance on the Montgomery County dataset further demonstrated its consistent effectiveness. Moreover, when applied to additional datasets of Chest X-ray Masks and Labels and COVID-19, our method maintained high performance levels, achieving up to 99.3% accuracy, thereby underscoring its adaptability and potential for broad applications in medical imaging diagnostics.

A Deep Learning Algorithm to Detect Covid-19 from Lung X-ray Images

Covid-19 Predictive Model Using Deep Learning algorithm on X-Ray images of different patients. The individuals have been infected with the virus in the recent years due to large population spreading of covid- 19 virus has increased, A real-time reverse transcriptase- polymerase chain reaction (RT-PCR) kit serves to identify the illness. This project provides another solution by implementing a model that distingue, the covid and normal patients by using lung X-Ray images. In this project we consider the following three dataset such as coronavirus pneumonia (NCP) contaminated by SARS-COV-2, common pneumonia (CP) and normal control to implement the deep learning model during predicting covid-19. These model gives better accuracy and efficiency in anticipating covid-19 infection.

Explaining transfer learning models for the detection of COVID-19 on X-ray lung images

Amidst the coronavirus disease 2019 (COVID-19) pandemic, researchers are exploring innovative approaches to enhance diagnostic accuracy. One avenue is utilizing deep learning models to analyze lung X-ray images for COVID-19 diagnosis, complementing existing tests like reverse transcription polymerase chain reaction (RT-PCR). However, trusting these models, often viewed as black boxes, presents a challenge. To address this, six explainable artificial intelligence (XAI) techniques: local interpretable model agnostic explanations (LIME), Shapley additive explanations (SHAP), integrated gradients, smooth-grad, gradient-weighted class activation mapping (Grad-CAM), and Layer-CAM are applied to interpret four transfer learning models. These models: VGG16, ResNet50, InceptionV3, and DenseNet121 are analyzed to understand their workings and the rationale behind their predictions. Validating the results with medical experts poses difficulties due to time and resource constraints, alongside the scarcity of annotated X-ray datasets. To address this, a voting mechanism employing different XAI methods across various models is proposed. This approach highlights regions of lung infection, potentially reducing individual model biases stemming from their structures. If successful, this research could pave the way for an automated system for annotating infection regions, bolstering confidence in predictions and aiding in the development of more effective diagnostic tools for COVID-19.

Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.

Lung X-ray image segmentation algorithm based on Multihead Self-Attention Mechanism (MSAG) optimizing Unet networks

In this paper, the Multihead Self-Attention Mechanism (MSAG) is used to optimize the Unet network for accurate segmentation of lung X-ray images. By introducing the MSAG module, the ability of the Unet network to capture global and local correlations is enhanced, which effectively improves the accuracy of the segmentation results. The introduction of the multi-head self-attention mechanism enables the network to have more powerful modelling and generalization capabilities, and can process various types of lung X-ray images stably and efficiently. The dataset is divided into training, validation and test sets according to the ratio of 4:3:3. The loss gradually converges during the training process, and the model gradually learns the data features and patterns, and the gap between them and the real labels is gradually reduced. The performance on the validation set is good and no over-fitting occurs, demonstrating the ability to generalize on unseen data. The evaluation metrics on the test set show an IoU of 0.85, a Dice of 0.92, and an Accuracy of 0.88, proving that the model can accurately extract lung features for segmentation. This study has achieved satisfactory results in the field of medical images by optimizing the network structure and introducing new techniques, which are of positive significance for improving the accuracy and efficiency of lung X-ray image segmentation.

Lung X-ray image segmentation algorithm based on Multihead Self-Attention Mechanism (MSAG) optimizing Unet networks

In this paper, the Multihead Self-Attention Mechanism (MSAG) is used to optimize the Unet network for accurate segmentation of lung X-ray images. By introducing the MSAG module, the ability of the Unet network to capture global and local correlations is enhanced, which effectively improves the accuracy of the segmentation results. The introduction of the multi-head self-attention mechanism enables the network to have more powerful modelling and generalization capabilities, and can process various types of lung X-ray images stably and efficiently. The dataset is divided into training, validation and test sets according to the ratio of 4:3:3. The loss gradually converges during the training process, and the model gradually learns the data features and patterns, and the gap between them and the real labels is gradually reduced. The performance on the validation set is good and no over-fitting occurs, demonstrating the ability to generalize on unseen data. The evaluation metrics on the test set show an IoU of 0.85, a Dice of 0.92, and an Accuracy of 0.88, proving that the model can accurately extract lung features for segmentation. This study has achieved satisfactory results in the field of medical images by optimizing the network structure and introducing new techniques, which are of positive significance for improving the accuracy and efficiency of lung X-ray image segmentation.

Detection of COVID-19: A Metaheuristic-Optimized Maximally Stable Extremal Regions Approach

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration of alternative approaches, including the analysis of lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in X-ray images indicative of COVID-19 by combining the maximally stable extremal regions (MSER) method with metaheuristic algorithms. The MSER method is efficient and effective under various adverse conditions, utilizing symmetry as a key property to detect regions despite changes in scaling or lighting. However, calibrating the MSER method is challenging. Our approach transforms this calibration into an optimization task, employing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), and Genetic Algorithms (GA) to find the optimal parameters for MSER. By automating the calibration process through metaheuristic optimization, we overcome the primary disadvantage of the MSER method. This innovative combination enables precise detection of abnormal regions characteristic of COVID-19 without the need for extensive datasets of labeled training images, unlike deep learning methods. Our methodology was rigorously tested across multiple databases, and the detection quality was evaluated using various indices. The experimental results demonstrate the robust capability of our algorithm to support healthcare professionals in accurately detecting COVID-19, highlighting its significant potential and effectiveness as a practical and efficient alternative for medical diagnostics and precise image analysis.

Advanced Lung Disease Detection and Classification Using Ge-U-Net-ODLwith Gabor Filters and Entropy-Based Feature Selection

Lung X-ray images play a crucial role in the detection and diagnosis of various lung diseases,including pneumonia, tuberculosis, and lung cancer. These images provide a non-invasive method for visualizing lung structures, allowing radiologists and machine learning models to identify abnormalities such as nodules, masses, or fluid accumulation. With the advancement of deep learning techniques, lung X-ray images are now used in automated systems that can detect and classify diseases with high accuracy. By applying sophisticated algorithms like GE-U-Net-ODL, Gabor filters, and entropy-based feature extraction, these images are analyzed pixel by pixel to enhance feature representation and improve diagnostic precision. This paper presents a novel approach for lung disease detection and classification using the GE-U-Net-ODL model, which integrates advanced preprocessing techniques and deep learning architectures. The study leverages the NIH Chest X-ray dataset and employs a variety of feature extraction and selection methods, including Gabor filters, entropy-based techniques, and multi-scale inputs. A detailed comparative analysis of different model configurations demonstrates that the GE-U-Net-ODL model with Transfer Learning achieves the highest classification accuracy of 95.0%, alongside superior precision (93.5%), recall (94.0%), and F1-score (93.7%). Other configurations, such as those utilizing data augmentation and hybrid filters, also showed notable performance improvements. The research underscores the model's effectiveness in enhancing diagnostic accuracy for lung diseases while balancing training and inference times.

Removing Adversarial Noise in X-ray Images via Total Variation Minimizationand Patch-Based Regularization for Robust DeepLearning-based Diagnosis.

Deep learning has significantly advanced the field of radiology-based disease diagnosis, offering enhanced accuracy and efficiency in detecting various medical conditions through the analysis of complex medical images such as X-rays. This technology's ability to discern subtle patterns and anomalies has proven invaluable for swift and accurate disease identification. The relevance of deep learning in radiology has been particularly highlighted during the COVID-19 pandemic, where rapid and accurate diagnosis is crucial for effective treatment and containment. However, recent research has uncovered vulnerabilities in deep learning models when exposed to adversarial attacks, leading to incorrect predictions. In response to this critical challenge, we introduce a novel approach that leverages total variation minimization to combat adversarial noise within X-ray images effectively. Our focus narrows to COVID-19 diagnosis as a case study, where we initially construct a classification model through transfer learning designed to accurately classify lung X-ray images encompassing no pneumonia, COVID-19 pneumonia, and non-COVID pneumonia cases. Subsequently, we extensively evaluated the model's susceptibility to targeted and un-targetedadversarial attacksby employing the fast gradient sign gradient (FGSM) method. Our findings reveal a substantial reduction in the model's performance, with the average accuracy plummeting from 95.56 to 19.83% under adversarial conditions. However, the experimental results demonstrate the exceptional efficacy of the proposed denoising approach in enhancing the performance of diagnosis models when applied to adversarial examples. Post-denoising, the model exhibits a remarkable accuracy improvement, surging from 19.83 to 88.23% on adversarial images. These promising outcomes underscore the potential of denoising techniques to fortify the resilience and reliability of AI-based COVID-19 diagnostic systems, laying the foundation for their successful deployment in clinical settings.

TO ANALYZE THE LUNGS X-RAY IMAGES USING MACHINE LEARNING ALGORITHM: AN IMPLEMENTATION TO PNEUMONIA DIAGNOSIS

Introduction: Respiratory diseases, particularly pneumonia, pose a significant threat to human life. Pneumonia affects the respiratory function in the human body and is a dangerous lung disease. This study aims to propose a model for detecting pneumonia in chest XR images. By utilizing statistical-based features, relevant and informative features are extracted from lung X-ray images. Objective: The objective is to obtain high accuracy in pneumonia identification; the target of this work is to generate a model that can precisely recognize the presence of pneumonia by evaluating chest X-ray pictures. Method: The Method follows a three-phase approach: preprocessing, categorization, and extraction of features. Preprocessing is the stage when various filters are applied to the chest X-ray images to enhance their eminence and eradicate noise. The feature extraction phase involves extracting statistical-based features from the preprocessed images. These features capture relevant information regarding a pneumonia diagnosis. Finally, in the classification phase, algorithms for machine learning are employed to use the retrieved features to categorize the X-ray pictures as infected or uninfected. Result: The proposed model successfully detects the presence of pneumonia accurately. By leveraging advanced machine learning algorithms, the model achieves accurate X-ray image classification for the chest. Conclusion: This study concludes by presenting a model for detecting pneumonia by examining chest X-ray pictures. To accurately classify infected and non-infected lungs, the proposed model makes use of image dispensation methods and machine learning algorithms. The model's high accuracy in pneumonia detection can significantly contribute to early diagnosis and treatment.

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Tuberculosis Detection and Air Purifier Suggestion

Tuberculosis (TB) is a disease that can be fatal if not promptly treated. Ensemble deep learning methods have shown promise in aiding the early detection of TB. Previous research typically trained ensemble classifiers using images with similar features, but for optimal performance, an ensemble requires a range of errors, achievable through diverse classification techniques or feature sets. This study focuses on the latter approach, presenting TB detection using deep learning alongside contrast-enhanced canny edge detected (CEED-Canny) X-ray images. CEED-Canny was employed to generate edge-detected lung X-ray images. Two sets of features were derived: one from the enhanced X- ray images and the other from the edge-detected images. By introducing this variation in features, the diversity of errors among the base classifiers was increased, resulting in improved TB detection. The proposed ensemble method achieved a comparable accuracy of 93.59%, sensitivity of 92.31%, and specificity of 94.87% compared to prior research.

A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images

Machine intelligence has the potential to play a significant role in diagnosing, managing, and guiding the treatment of disease, which supports the rising demands on healthcare to provide rapid and accurate interpretation of clinical data. The global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus (SARSCoV-2) exposed a need for rapid clinical data interpretation in response to an unprecedented burden on the healthcare system. A new healthcare challenge has arisen – post-COVID syndrome or ‘long COVID’. Symptoms of the post-COVID syndrome can persist for months following infection with SARS-CoV-2, often characterised by fatigue, breathlessness, dizziness, and pain. Despite this additional healthcare burden, no tests can diagnose, monitor, or determine the efficacy of treatments/interventions to support recovery. In this paper, an array of machine-learning algorithms is trained to evaluate and detect COVID-19-associated changes to lung tissue from X-ray images. X-ray images are classified from open sources into three categories: COVID-19 patients, patients with pneumonia, and unaffected otherwise healthy individuals using existing Machine Learning (ML) and pre-trained deep learning models. Prioritising models with the fewest false positives and false negatives assessed the performance of different models in detecting COVID-19-associated lung tissue. In addition, image pre-processing, data augmentation, and hyperparameter tuning are used to achieve the best accuracy in the models. Different ML models, including K Nearest Neighbour (KNN), and decision trees (DT), as well as transfer learning models such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG-16, VGG-19), ResNet50, DenseNet201, Xception, and InceptionV3, were tested to evaluate the performance of these models for X-ray images classification. The comparative analysis indicates that VGG-19 with augmentation performed best among the ten algorithms with a training accuracy of 99%, testing accuracy of 98%, and precision of 90% for COVID-19, 90% for normal, and 100% for pneumonia. This higher accuracy for detecting COVID-19-associated lung changes on X-ray may be further developed to stratify patients suffering from post-COVID syndrome. This may enable future intervention studies to determine the efficacy of treatments or better track patients’ prognoses to be optimised.

Optimizing Pneumonia Diagnosis Using RCGAN-CTL: A Strategy for Small or Limited Imaging Datasets

In response to the urgent need for efficient pneumonia diagnosis—a significant health challenge that has been intensified during the COVID-19 era—this study introduces the RCGAN-CTL model. This innovative approach combines a coupled generative adversarial network (GAN) with relativistic and conditional discriminators to optimize performance in contexts with limited data resources. It significantly enhances the efficacy of small or incomplete datasets through the integration of synthetic images generated by an advanced RCGAN. Rigorous evaluations using a wide range of lung X-ray images validate the model’s effectiveness. In binary classification tasks that differentiate between normal and pneumonia cases, RCGAN-CTL demonstrates exceptional accuracy, exceeding 99%, with an area under the curve (AUC) of around 95%. Its capabilities extend to a complex triple classification task, accurately distinguishing between normal, viral pneumonia, and bacterial pneumonia, with precision scores of 89.9%, 95.5%, and 90.5%, respectively. A notable improvement in sensitivity further evidences the model’s robustness. Comprehensive validation underscores RCGAN-CTL’s superior accuracy and reliability in both binary and triple classification scenarios. This advancement is pivotal for enhancing deep learning applications in medical diagnostics, presenting a significant tool in addressing the challenges of pneumonia diagnosis, a key concern in contemporary healthcare.

Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging.

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

Advanced image generation for cancer using diffusion models.

Deep neural networks have significantly advanced the field of medical image analysis, yet their full potential is often limited by relatively small dataset sizes. Generative modeling, particularly through diffusion models, has unlocked remarkable capabilities in synthesizing photorealistic images, thereby broadening the scope of their application in medical imaging. This study specifically investigates the use of diffusion models to generate high-quality brain MRI scans, including those depicting low-grade gliomas, as well as contrast-enhanced spectral mammography (CESM) and chest and lung X-ray images. By leveraging the DreamBooth platform, we have successfully trained stable diffusion models utilizing text prompts alongside class and instance images to generate diverse medical images. This approach not only preserves patient anonymity but also substantially mitigates the risk of patient re-identification during data exchange for research purposes. To evaluate the quality of our synthesized images, we used the Fréchet inception distance metric, demonstrating high fidelity between the synthesized and real images. Our application of diffusion models effectively captures oncology-specific attributes across different imaging modalities, establishing a robust framework that integrates artificial intelligence in the generation of oncological medical imagery.

Generative and Discriminative Learning for Lung X-Ray Analysis Based on Probabilistic Component Analysis.

The paper presents a hybrid generative/discriminative classification method aimed at identifying abnormalities, such as cancer, in lung X-ray images. The proposed method involves a generative model that performs generative embedding in Probabilistic Component Analysis (PrCA). The primary goal of PrCA is to model co-existing information within a probabilistic framework, with the intent to locate the feature vector space for X-ray data based on a defined kernel structure. A kernel-based classifier, grounded in information-theoretic principles, was employed in this study. The performance of the proposed method is evaluated against nearest neighbour (NN) classifiers and support vector machine (SVM) classifiers, which use a diagonal covariance matrix and incorporate normal linear and non-linear kernels, respectively. The method is found to achieve superior accuracy, offering a viable solution to the class of problems presented. Accuracy rates achieved by the kernels in the NN and SVM models were 95.02% and 92.45%, respectively, suggesting the method's competitiveness with state-of-the-art approaches.

Analysis Influence Segmentation Image on Classification Image X-raylungs with Method Convolutional Neural

The impact of image segmentation on the classification of lung X-ray images using Convolutional Neural Networks (CNNs) has been scrutinized in this study. The dataset used in this research comprises 150 lung X-ray images, distributed as 78 for training, 30 for validation, and 42 for testing. Initially, image data undergoes preprocessing to enhance image quality, employing adaptive histogram equalization to augment contrast and enhance image details. The evaluation of segmentation's influence is based on a comparison between image classification with and without the segmentation process. Segmentation involves the delineation of lung regions through techniques like thresholding, accompanied by various morphological operations such as hole filling, area opening, and labeling. The image classification process employs a CNN featuring 5 convolution layers, the Adam optimizer, and a training period of 30 epochs. The results of this study indicate that the X-ray image dataset achieved a classification accuracy of 59.52% in network testing without segmentation. In contrast, when segmentation was applied to the X-ray image dataset, the accuracy significantly improved to 73.81%. This underscores the segmentation process's ability to enhance network performance, as it simplifies the classification of segmented image patterns.