Description:FIELD OF THE INVENTION
The present invention relates to the field of medical imaging and diagnostic systems, specifically focusing on the automated detection and classification of pneumonia from chest X-ray images. It involves the integration of advanced machine learning techniques, including deep learning architectures and optimization algorithms, to improve the accuracy and efficiency of pneumonia diagnosis.
BACKGROUND OF THE INVENTION
An automated system for identifying pneumonia in chest X-ray images introduces a novel framework that integrates the EfficientNet deep learning architecture for feature extraction, employs a Support Vector Machine (SVM) classifier for binary classification, and enhances performance using the Eagle Optimization Algorithm. This system aims to enhance healthcare by providing more accurate, efficient, and resource-effective diagnostics.
Pneumonia identification through medical imaging, particularly chest X-rays, is a critical challenge in healthcare due to its widespread occurrence and potential severity. Currently, healthcare professionals manually analyze these images, a process that is both time-consuming and subjective, leading to inconsistencies and delays in diagnosis. Traditional automated methods often fall short of the accuracy needed for reliable detection, which can result in misdiagnoses and compromised patient care.
Challenges:
1. Subjectivity and Variability: Interpretations of chest X-rays can vary among doctors, leading to inconsistent diagnoses.
2. Time-Consuming Process: The manual analysis of chest X-rays is labour-intensive and delays the diagnosis and treatment of pneumonia.
3. Limited Accuracy of Automated Methods: Existing automated methods may not provide the accuracy necessary for reliable diagnoses, leading to potential misdiagnoses and unnecessary interventions.
To overcome these challenges, our system combines EfficientNet's advanced feature extraction capabilities with the discriminative power of SVM. Additionally, the incorporation of the Eagle Optimization Algorithm enhances both classification accuracy and computational efficiency. This innovative approach offers improved precision, effectiveness, and scalability compared to current methodologies, representing a promising advancement for the early and accurate detection of pneumonia.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
Disclosed herein a hybrid model system for pneumonia detection from chest X-ray images, comprising:
a) Image Input Module: A component for receiving chest X-ray images for classification;
b) Data Set: A dataset sourced from the Kaggle platform, consisting of 5,856 chest X-ray images, categorized into training, testing, and validation datasets;
c) EfficientNet Architecture: A convolutional neural network designed for feature extraction, employing compound scaling to optimize accuracy, time complexity, and space complexity;
d) Eagle Optimization Algorithm (EOA): An optimization algorithm based on the hunting behavior of eagles, which iteratively refines solutions by assessing fitness and adjusting solutions in subsequent cycles;
e) Support Vector Machine (SVM) Classifier: A supervised learning algorithm for classifying features into binary categories by finding the optimal hyperplane that maximizes class separation;
f) Output Module: A component that generates final classification results, indicating whether the analyzed image represents pneumonia or a normal chest X-ray, and presents results as binary classifications or probability scores, with performance metrics;
g) Web Interface: A web application that facilitates pneumonia detection using the integrated model, allowing users to load images for analysis and receive results in an easily interpretable format.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: ARCHITECTURE OF HYBRID MODEL
FIGURE 2 ILLUSTRATES THE DISTRIBUTION OF THE DATASET
FIGURE 3: SAMPLE CHEST X-RAY IMAGES OF PNEUMONIA AND NORMAL CASES
FIGURE 4. CLASSIFICATION REPORT GENERATED BY EFFICIENTNET + EAGLE OPTIMIZATI
FIGURE 5. CLASSIFICATION REPORT GENERATED BY EFFICIENTNET WITH EAGLE OPTIMIZATION AND SVM
FIGURE 6: CONFUSION MATRIX FOR TEST DATASET (1171 SAMPLE IMAGES) GENERATED BY MODELS
FIGURE 7 ILLUSTRATES THE TRAINING AND VALIDATION ACCURACY AND LOSS GENERATED BY THE MODEL EFFICIENTNET WITH EAGLE OPTIMIZATION
FIGURE 8 DISPLAYS THE RESULTS GENERATED BY EFFICIENTNET WITH EAGLE OPTIMIZATION AND SVM
FIGURE 9 DISPLAYS THE ACCURACY GENERATED BY THE MODELS
FIGURE 10 DISPLAYS THE CLASSIFICATION REPORT GENERATED BY THE MODELS FOR CLASS 0(NORMAL)
FIGURE 11 DISPLAYS THE CLASSIFICATION REPORT GENERATED BY THE MODELS FOR CLASS 1(PNEUMONIA)
FIGURE 12: FLOWCHART OF EAGLE OPTIMIZATION ALGORITHM
FIGURE 13: REPRESENTS HOME PAGE OF THE WEB APPLICATION
FIGURE 14: THE RESULTS ARE DISPLAYED IN REAL-TIME WITH CLEAR, COLOR-CODED MESSAGES INDICATE PNEUMONIA OR NORMAL CONDITIONS
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Pneumonia is a common respiratory infection that presents substantial health hazards, especially in susceptible populations like children and the elderly, Quick identification is essential for efficient management and therapy of Pneumonia. The proposed hybrid model architecture, as illustrated in Figure 1, combines EfficientNet, the Eagle Optimization algorithm, and an SVM classifier. It consists of several key components, including:
• Image Input Module: This module receives input images of X-ray images for classification.
• Data set description: The dataset collected from the public domain Kaggle platform, and comprises 5,856 chest X-ray images aimed at detecting pneumonia. It encompasses both normal and pneumonia-affected lung images, facilitating the development and assessment of machine learning techniques for pneumonia detection. Dataset is organized into three main categories such as train_dataset, test_dataset, and validation_dataset shown in figure 2, each dataset features subfolders for pneumonia and normal images. The train_dataset category encompasses 5,216 X-ray images, with 3,875 depicting pneumonia cases and 1,341 representing normal lung conditions. In the test_dataset category, there are 624 X-ray images, including 390 pneumonia images and 234 normal images. The validation dataset category contains 16 X-ray images, with 8 depicting pneumonia cases and 8 representing normal lung conditions. Normal chest X-ray images show a clear sputum without any necrosis, whereas chest X-ray images usually show focal lobar consolidation suggestive of infection Dataset aims to train machine learning models for pneumonia diagnosis, . validation and testing, enabling the development of accurate and efficient algorithms capable of automatically detecting pneumonia from chest X-ray images The example data set is illustrated in Figure 3 , where chest X-rays a used to train, test, and validate the proposed hybrid model. Summarized images are displayed Furthermore, Figure 2 shows a dataset divided into different categories, such as pneumonia and normal cases, which provides a visual representation of the data distribution in different categories
• EfficientNet Architecture: EfficientNet, is a new generation of convolutional neural network commonly known as CNN and has been developed to offer efficient performance than its predecessors. It solves the problem of getting a trade-off between accuracy, time complexity, and space complexity which are very important when deploying Deep learning in scenarios that require low computing power. The most important breakthrough introduced in EfficientNet was a new compound scaling method, the element of the compound scaling method includes Depth Scaling, Width Scaling, and Resolution Scaling. Thus, simultaneously increasing depth, width, and resolution, EfficientNet provides the best results on the image classification task and, at the same time, is more efficient compared to previous architectures like ResNet or Inception.
• Eagle Optimization Module(EOA): The EOA – an optimization algorithm derived from the hunting movement of eagles. It first creates a starting set of solutions, assesses the solutions’ fitness, and cyclically searches for solutions within the problem and adjusts the solutions in the subsequent cycles to refine a solution. At last, it displays the best solution found after the optimization process has been performed and the flow chart of the used algorithm known as the Eagle Optimization Algorithm (EOA) as depicted in fig 12.
• Support Vector Machine classifier: The last step related to the classification of the features is performed by the SVM classifier. SVM is one of the portion of supervised learning that is efficient in classifying binary classifiers. It does this by identifying the right hyperplane that would yield the greatest distance between the classes in the feature space.
• Output Module: The output module produces the final classification outcomes; if the image being analyzed represents a condition with pneumonia or not a normal chest X-ray. These results can either be presented in the form of binary classification or the form of probability scores. Looking at the figures 4, 5 and 6 it is apparent that the proposed model produced the best results of the three models under study and proved the efficiency of the described hybrid approach in diagnose pneumonia from chest X-ray images. These figures depict several indices with regards to the performance of the model including accuracy, precision, recall, F1 score to confirm the reliability of the model for clinical applications.
By integrating these components, the detection system combines the strengths of EfficientNet for feature extraction, the Eagle Optimization Algorithm for feature optimization, and SVM for classification to achieve accurate and efficient pneumonia detection from chest X-ray images. The proposed hybrid model outperformed, achieving 99% precision for class 1 (Pneumonia) and 99% recall for class 0 (Normal).
• Web Interface : It is an effective web application to help the users to detect pneumonia using state of the art machine learning. For feature extraction, it employs EfficientNet, for classification, it uses the SVM algorithm, and for feature selection, an Eagle Optimization Algorithm is used for Chest X-ray image analysis. It is also rather simple to load images (as depicted in figure 13), which in turn are analyzed to depict normal or pneumonia conditions. The results are projected concurrently and include simple, easily distinguishable definitive messages as highlighted in figure 14. Hence, the application combines high accuracy and speed to make it easier for the healthcare professionals to diagnose pneumonia effectively and in a shorter time.
Data Flow and Workflow:
The implementation of this solution involves the following steps:
1. Initialization: Initialize the EfficientNet, Support Vector Machine architecture and Eagle Optimization Algorithm parameters.
2. Data Collection and Preprocessing: Relevant medical imaging data, such as X-rays or CT scans, containing images of lungs with and without pneumonia, are collected from public domain Kaggle dataset. These images are preprocessed to enhance their quality and remove noise, ensuring better performance during training and testing.
3. Data Splitting: Split the dataset into training,testing and validation sets to facilitate model training and evaluation.
4. Model Training: In this case, train the Hybrid model by combining efficient net and SVM classifier. Optimize the model parameters using the Eagle Optimization Algorithm to improve high classification rate.
a. Feature Extraction with EfficientNet: The EfficientNet architecture is used to extract the appropriate features from the processed input images. The network has prior exposure to a large registry, typically ImageNet, to acquire normal image representations before being trained to convey medical imaging data specifically for pneumonia detection.
b. Classification with SVM: The output features of the EfficientNet model are passed to an SVM classifier for classification. From the features learned by EfficientNet, the SVM learns the separability of pneumonia cases from the non-pneumonia cases. The parameters related to the SVM could be tuned with the help of the EOA so as to enhance the performance of the model.
5. Model Evaluation and Validation: Hence, accuracy, precision, recall, and F1-score are used to assess the efficiency of the integrated EfficientNet-SVM. Out-of-sample methods can be applied to ensure that the model is not over-fitted on the data.
6. Deployment and Integration: Once the model is trained and validated, it can be deployed in a clinical setting where it can assist healthcare professionals in the early and accurate detection of pneumonia from medical images.
By combining the capabilities of EfficientNet for feature extraction, SVM for classification, and the optimization power of the Eagle Optimization Algorithm, this invention offers a promising solution for improving pneumonia detection accuracy and efficiency in medical diagnostics.
NOVELTY:
The proposed work, which is “Pneumonia Detection using EfficientNet, Support Vector Machine, and Eagle Optimization Algorithm”, is innovative in the sense that three different parts make up the whole system to enable the efficient detection of pneumonia from images of chest X-Ray. Here are some aspects that contribute to the novelty of the approach. Here are some aspects that contribute to the novelty of the approach:
1. Integration of Traditional Machine Learning and Deep Learning Techniques: The proposed system integrates EfficientNet for feature extraction, and Support Vector Machine for classification. This approach uses the feature of both paradigms to obtain a better and more reliable classification of pneumonia.
2. Feature Optimization using the Eagle Optimization Algorithm: Interesting to note is the proposed addition of a feature optimization technique in the form of Eagle Optimization Algorithm in the system. Unlike the conventional methods that requires the user to define the features or even selects the features manually, the algorithm learns the features from the EfficientNet model and optimizes the features for classification in real-time which helps to boost the discriminative ability of the model’s features.
3. EfficientNet for Chest X-ray Image Analysis: For image analysis tasks the model of the deep learning architecture called EfficientNet is used. The proposed method applied for chest X-ray images for pneumonia detection is new and establishes the feasibility of utilizing state-of-art deep learning approaches in the medical image analysis.
ADVANTAGES OF THE INVENTION
1. High Accuracy: Due to the fine feature extraction by EfficientNet and the good discriminative ability of the Support Vector Machine classifier, the system’s recognition rate of pneumonia in chest X-ray images is generally high. The enhancement by the Eagle Optimization Algorithm also positively influences the feature selection’s feature, promoting better classification results.
2. Efficient Feature Extraction: EfficientNet is that it is created to hit the right trade-off between the scale of the model and its accuracy which makes it effective for feature extraction from chest Xray images. This makes it possible for the system to learn and memorize patterns and characteristics connected with pneumonia, and at the same time is not very resource and memory consuming.
3. Effective Feature Optimization: The Eagle Optimization Algorithm refines the feature set of EfficientNet where the algorithm first selects the top features out of the EfficientNet feature set for classification. This increases the discrimining power of the features and also results in an improvement of the system’s performance when discriminating between pneumonia and normal cases by increasing the accuracy and general robustness of the system.
4. Synergistic Integration: With the use of deep learning, optimization algorithms and traditional machine learning the system has the advantage of an interaction between these three means. Every individual part improves the general performance of the system such that the detection of pneumonia is comprehensive than when each of the components is tackled separately.
5. Potential for Automation and Efficiency: After that, it is possible to set the identified system as an algorithm that can recognize pneumonia, which means diminishing the need for radiologists and other healthcare specialists to interpret pictures of chest X-ray manually. This can result in the enhancement of the diagnostics’ efficacy, time required to process the scans, and overall patient success.
6. Enhanced Diagnostic Support: Thus, the proposed system can be concluded as beneficial in helping healthcare practitioners, as a diagnostic support tool, and ensuring highly accurate and precise predictions regarding pneumonia detection. It can help clinicians in obtaining objective results from chest Xray images which may in turn help them in decision making and or prioritization of patients’ care needs.
The comparison of two pneumonia detection models that are based on EfficientNet architecture shows the differences in the performance metrics. The first model, where EfficientNet with Eagle optimization was used, proved to be highly accurate as 97% was shown in figure 9. This model was specifically very accurate in terms of precision and recall for both pneumonia and normal classes with 96% for the normal class and 97% for pneumonia class thus implying that the model is very reliable in diagnosing both conditions. However, the second model that was created with integration of EfficiencyNet, Eagle optimization, and SVM attained the second highest accuracy of 93 percent only. Using this model the precision and recall were still effective, where 80% of the normal cases were precise while 99% of pneumonia cases were precise. In conclusion, it can be stated that the usage of EfficientNet and Eagle optimization has been proven to be promising for increasing the pneumonia detection performance and maintaining the high accuracy at the same time.
, C , C , Claims:1. A hybrid model system for pneumonia detection from chest X-ray images, comprising:
a) Image Input Module: A component for receiving chest X-ray images for classification;
b) Data Set: A dataset sourced from the Kaggle platform, consisting of 5,856 chest X-ray images, categorized into training, testing, and validation datasets;
c) EfficientNet Architecture: A convolutional neural network designed for feature extraction, employing compound scaling to optimize accuracy, time complexity, and space complexity;
d) Eagle Optimization Algorithm (EOA): An optimization algorithm based on the hunting behavior of eagles, which iteratively refines solutions by assessing fitness and adjusting solutions in subsequent cycles;
e) Support Vector Machine (SVM) Classifier: A supervised learning algorithm for classifying features into binary categories by finding the optimal hyperplane that maximizes class separation;
f) Output Module: A component that generates final classification results, indicating whether the analyzed image represents pneumonia or a normal chest X-ray, and presents results as binary classifications or probability scores, with performance metrics;
g) Web Interface: A web application that facilitates pneumonia detection using the integrated model, allowing users to load images for analysis and receive results in an easily interpretable format.
2. The hybrid system as claimed in claim 1, wherein the dataset is organized into three categories (train_dataset, test_dataset, and validation_dataset), with subfolders for pneumonia and normal images to support model training, validation, and testing.
3. The hybrid system as claimed in claim 1, wherein EfficientNet Architecture Uses depth scaling, width scaling, and resolution scaling to improve image classification accuracy while maintaining computational efficiency.
4. The hybrid system as claimed in claim 1, wherein Eagle Optimization Algorithm (EOA) Provides feature optimization by refining feature sets extracted by EfficientNet, thereby enhancing the discriminative power of the classification model.
5. The hybrid model of claim 1, wherein Support Vector Machine (SVM) Classifier Receives features extracted by EfficientNet and performs binary classification to distinguish between pneumonia and normal chest X-ray images.
6. The hybrid system as claimed in claim 1, wherein Output Module Generates results that include accuracy, precision, recall, and F1-score, demonstrating the model's efficiency and reliability for clinical applications.
7. The hybrid model of claim 1, wherein Web Interface Simplifies the process of pneumonia detection by allowing users to upload X-ray images, which are then analyzed using the integrated EfficientNet, SVM, and EOA components, with results displayed in a user-friendly format.
8. The hybrid system as claimed in claim 1, wherein Data Flow and Workflow Includes steps of initialization, data collection and preprocessing, data splitting, model training, feature extraction, classification, evaluation, validation, and deployment to assist in the early and accurate detection of pneumonia from medical images.
9. A method for detecting pneumonia in chest X-ray images, comprising the steps of:
a) Receiving: Chest X-ray images through an Image Input Module;
b) Processing: The images using EfficientNet for feature extraction;
c) Optimizing: Features using the Eagle Optimization Algorithm;
d) Classifying: The optimized features with a Support Vector Machine classifier;
e) Generating: Classification results and performance metrics through an Output Module;
f) Displaying: Results via a Web Interface for user interaction.
10. The method as claimed in claim 1, wherein Data Collection: Uses a Kaggle dataset comprising categorized X-ray images for training, validation, and testing to develop and assess the pneumonia detection model.