Disease Detection System Research Articles

Developing an early detection model for skin diseases using a hybrid deep neural network to enhance health independence in coastal communities

The object of study is a solution for early skin disease diagnosis by integrating hybrid deep neural networks – EfficientNetB7 for Classification and YOLOv8 for detection. The system is designed to classify five skin conditions: Melanoma, Basal Cell Carcinoma (BCC), melanoma is a type of skin cancer that originates from melanocytes, the cells that produce skin pigment, Melanocytic Nevi (NV) Melanocytic nevus is a mole or dark spot on the skin formed due to the accumulation of melanocytes, Benign Keratosis-like Lesions (BKL) is a term for a group of skin changes that resemble keratosis but are non-cancerous, and Seborrheic Keratoses and other benign tumors to enhance the health diagnostics. The problem to be solved in this study revolves around improving early and accurate skin disease diagnosis, particularly in resource-limited or underserved areas and the lack of Accessible Diagnostic Tools and Low Efficiency of Current Diagnostic Methods. The study highlights EfficientNetB7's classification accuracy at 94 % and YOLOv8's means average precision (mAP) of 0.812 for detection. This hybrid system processes skin images efficiently, providing classification and detection outcomes with consistent performance in multiple tests. The results demonstrate that the EfficientNetB7 model achieved an accuracy of 94 % on test data, while YOLOv8 delivered a detection performance with a mean average precision (mAP) of 0.812. The web-based system efficiently processed skin images and provided classification and detection outcomes. Furthermore, integrating EfficientNetB7 and YOLOv8 allowed the skin disease detection system to classify five different diseases and assess malignancy risk. The systems are portable and can be used with minimal setup, making them practical for real-world diagnostic use. The Scope and Practical applications are designed for accessibility in resource-limited settings. The website-based skin disease detection tool provides a user-friendly platform accessible to the public and healthcare providers, especially in underserved areas. Each application's high accuracy and ease of use make them viable aids in early diagnosis, potentially improving healthcare access

A Comprehensive Review of Disease Detection Techniques for Tomato Leaves

Tomato plants plays vital role in global agriculture, significantly impacting food security and economic stability. However, diseases affecting tomato leaves present substantial challenges to crop yields and quality, highlighting the need for effective detection methods. This paper presents a comprehensive review of disease detection techniques for tomato leaves, emphasizing the transformative impact of advancements in image processing, machine learning, and deep learning. Approaches are categorized based on their methodologies, including traditional image processing, machine learning, and cutting-edge deep learning frameworks. Key concepts such as disease segmentation, feature extraction, and transfer learning are defined to provide a foundational understanding. The review also identifies critical research gaps, particularly concerning the generalizability of solutions to real-world conditions and the necessity for computational efficiency in field applications. Organized by method categories, evaluation metrics, and dataset utilization, this review encompasses recent advancements up to 2024, focusing on improving accuracy, scalability, and practical implementation. Ultimately, this work aims to serve as an insightful reference for researchers and practitioners, facilitating the advancement of disease detection systems for tomato leaves for real-world deployment

Residual network-based feature extraction for automatic crop disease detection system using drone image dataset

PurposeDiagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.Design/methodology/approachThe proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one.FindingsThese proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India.Originality/valueThe proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.

Skin Disease Detection Using Ensemble Learning

Skin diseases affect millions of people worldwide, making early detection and accurate diagnosis crucial for effective treatment. However, the process of diagnosing skin conditions often requires specialized dermatological expertise, which can be time-consuming and expensive. To address this challenge, we have developed a web-based Skin Disease Detection System using cutting-edge machine learning and ensemble techniques. Our system leverages the HAM10000 dataset, consisting of 10,000 dermoscopic images representing seven common skin lesion types. We employ an ensemble learning approach, integrating ResNet50, EfficientNetB0, and DenseNet121 as base models and combining their predictions using a logistic regression meta-model. This architecture enhances the accuracy and robustness of the predictions. The backend of our application is built with Flask, responsible for model processing, authentication, and handling API requests. The React frontend provides an intuitive user interface where users can upload skin lesion images and receive real-time predictions along with confidence scores. The system also incorporates authentication and token verification for secure user access, ensuring data privacy and integrity. Our project aims to provide an accessible, reliable, and cost-effective tool to assist healthcare professionals and individuals in the early detection of skin diseases, potentially improving patient outcomes and reducing the burden on dermatology services

AI Horizons in Indian Healthcare: A Vision for Transformation and Equity

Artificial intelligence (AI) is poised to revolutionize healthcare delivery in India, offering solutions to address the nation’s unique healthcare challenges. This position paper, presented by the Indian Association of Preventive and Social Medicine, examines the integration of AI in Indian healthcare, exploring its applications across diagnostic imaging, patient care, medical research, rehabilitation, and administrative processes. Notable implementations include AI-driven disease detection systems, telemedicine platforms, and public health surveillance tools, with successful applications in tuberculosis screening, breast cancer detection, and ophthalmological care. While these advancements show promise, significant challenges persist, related to data privacy concerns and interoperability issues, including the need for robust ethical frameworks. The paper highlights key stakeholder collaborations, including government initiatives and international partnerships, which are driving innovation in this space. Based on this analysis, we propose policy recommendations emphasizing research investment, professional training, and regulatory frameworks to ensure responsible AI adoption. Our vision advocates for an approach that balances technological advancement with accessibility and equity in healthcare delivery.

Automatic Detection of Skin Diseases Using Convolutional Neural Network Algorithms

Skin diseases are a major health concern in Indone sia and they can seriously impact a patient’s quality of life. The problem is aggravated by humid tropical climate, limited access to healthcare facilities, and a lack of trained dermatology personnel. The cases in Indonesia are many, and the diagnosis and treatment of skin diseases are delayed, which makes the patient's condition worse. Based on data from the Ministry of Health (Kemenkes), the prevalence of skin disease in Indonesia is 0.62 cases per 10,000 population with the highest prevalence in Eastern Indonesia. Developing a Skin Disease Detection System Based on Convolutional Neural Network (CNN) algorithms. However, CNN algorithms are widely used in image recognition and classification, and can act as an automatic diagnostic system. This system has been developed to aid in diagnosis and improve patient access to dermatological care, especially for remote communities. Users can reach out for services at any time and any location, a practical solution for treating skin health problems. This study's results are anticipated to lower the diagnostic delays and improve the treatment outcomes while offering quick access to reliable dermatological service. This is a great effort on global level for any skin disease supporting to improve life of human lives from skin health issues.

Plant Leaf Disease Detection Using Integrated Color and Texture Features

In the realm of precision agriculture, a pivotal challenge lies in the detection, identification, and grading of crop diseases. This multifaceted task necessitates the involvement of expert human resources and time-sensitive actions aimed at mitigating the risks of production losses and the rapid spread of diseases. The effectiveness of the majority of developed systems in this domain hinges on the quality of image features and disease segmentation accuracy. This paper presents a comprehensive research endeavor in the domain of Content-Based Image Retrieval (CBIR), specifically tailored to detect and classify leaf diseases. The proposed system integrates both color and texture features to underpin its functionality, providing a robust framework for accurate disease detection. By leveraging advanced image processing techniques, the system enhances the precision of disease identification, which is crucial for timely and effective intervention in agricultural practices. To evaluate the system’s performance, maize leaves afflicted by rust and blight serve as prime candidates for testing. These diseases were chosen due to their prevalence and significant impact on crop yield. The experimental results demonstrate that the developed system consistently excels in its disease detection and identification tasks, boasting an impressive efficiency rate of 98.33%. This high level of accuracy underscores the potential of the system to be a valuable tool in precision agriculture, aiding farmers and agricultural experts in maintaining healthy crops and optimizing production. The integration of color and texture features not only improves the detection accuracy but also provides a comprehensive understanding of the disease characteristics. This dual-feature approach ensures that the system can distinguish between different types of diseases with high precision, making it a versatile solution for various agricultural applications. The findings of this research highlight the importance of advanced image analysis techniques in enhancing the capabilities of disease detection systems, paving the way for more efficient and effective agricultural practices.

Artificial intelligence detection of refractive eye diseases using certainty factor and image processing

Refractive errors are defined as an impairment in the eye’s capacity to focus light, resulting in the formation of blurred or unfocused images. These issues arise from alterations in the shape of the cornea, the length of the eyeball, or the aging of the crystalline lens. It is anticipated that the prevalence of visual impairment will increase in conjunction with global population growth. At present, a significant number of countries have not yet accorded sufficient priority to eye health within their healthcare systems. This has resulted in insufficient awareness and reluctance to seek costly specialized care. This study proposes the development of an advanced refractive eye disease detection system with the objective of improving diagnostic accuracy, disseminating disease information, and reducing financial barriers to specialist consultation. The research employs certainty factor (CF) methods and image processing with feature extraction. The initial results demonstrate the potential for identifying specific refractive eye diseases with high certainty through the analysis of symptoms and the examination of photographs of the eye. The proposed approach provides an alternative method for diagnosing refractive eye diseases, which could enhance access to refractive eye care services and reduce the economic burden on patients.

Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning

The lung is a critical organ for blood gas exchange. Early lung disease detection is often hindered by subtle symptoms. The diagnosis typically requires expert analysis of lung image scans, where both conventional methods and advanced machine learning (ML) and deep learning (DL) techniques are employed for scan segmentation and disease detection. However, it is a challenge to develop reliable computational models, due to the scarcity of high-quality data. With a major emphasis on lung disease classification and segmentation performance, this work presents a novel data quality assessment technique specifically designed for lung image datasets. The proposed pipeline combines ensemble learning, unsupervised learning, and generative autoencoders with attention mechanisms (GAME). By including attentional mechanisms in the generative autoencoders, we improve the tool’s ability to locate and prioritise areas of interest in lung images and extract the right features, which increases the accuracy of our algorithms. The pipeline automates quality checks and reduces the need for high-quality data, while reducing human oversight. Because it’s crucial to validate the robustness of the algorithms in our tool, we tested it using lung scans from the NIH-ChestXray and CheXpert datasets, and obtained IOU scores of 0.88 and 0.86, F1 scores of 0.95 and 0.95, and accuracy scores of 0.96 and 0.95, respectively, which shows they can be used as a classification tool as well as a segmentation tool. Given the challenging conditions in which the early diagnosis of lung disease is made, this is a significant step forward in the development of self-sustaining and accurate disease detection systems. The proposed model is made available to the public and the same is accessible via https://github.com/harshivchandra/LungDataQualityAssessment.

Early Detection and Classification of Black Gram Plant Leaf Diseases Using ITL-CHB Method

The impact of black gram plant leaf diseases on global crop production is significant, resulting in considerable declines in the quantity and quality of agricultural commodities. Black gram is one of the most significant grain legumes in India, but its cultivation and production rate are heavily impacted by fungus attacks. To prevent the spread of black gram diseases, it is crucial to predict their presence in the premature stage through a computer-aided system. This paper proposes an automatic black gram plant disease (BPLD) detection system called “Improved Inception-V3 Transfer Learning based Crossover Honey Badger (ITL-CHB)” for disease detection and categorization. The BPLD dataset contains five different categories of plant disease images. In our BPLD dataset, several augmentation steps such as grayscale conversion, resizing, and intensity normalization are used to improve the data diversity and address the data imbalance issue. The pre-trained Inception-V3 model extracts features with well-defined hyperparameter values using the Crossover Honey Badger (CHB) algorithm. Thus, the proposed ITL-CHB approach predicts and classifies diseased and healthy leaf images with high accuracy. The effectiveness of the proposed model is examined using numerous performance metrics namely accuracy, sensitivity, precision, specificity, and f1-score. The comparative result demonstrates that the effectiveness of the ITL-CHB method achieved a higher accuracy rate of 98% compared to other existing techniques of SVM, Efficient Net, ResNet 50, and CNN in detecting and classifying black gram leaf diseases.

Classification of Chest Disease Detection Using X-Ray Images through the Implantation of Efficientnetv2

Abstract—Chest diseases encompass a broad spectrum of conditions that can severely affect both respiratory and cardiovascular health. These diseases may arise from various factors, including infections, environmental influences, genetic predispositions, and lifestyle choices. To address these challenges, this project proposes a system for chest disease detection using X-ray images, employing the EfficientNet V2 model.The methodology begins with preprocessing steps, where an Adaptive Median Filter is used to enhance the quality of X-ray images by reducing noise while preserving important features for analysis. After preprocessing, Fuzzy C-Means (FCM) Image Segmentation is applied to precisely identify key regions within the images, improving the robustness of the classification model. EfficientNet V2 is then employed to extract features and classify the images, offering improved accuracy compared to traditional methods.Additionally, Local Binary Patterns (LBP) are incorporated for feature extraction, further enriching the input data for the model.This proposed framework aims to automate the diagnostic process, reducing the reliance on manual interpretation by radiologists. Experimental results demonstrate the effectiveness of this approach in accurately identifying and classifying chest diseases, thereby contributing to faster and more efficient patient care. The entire project is implemented using Python. Keywords—Fuzzy C-Means (FCM),Local Binary Patterns (LBP), Convolutional Neural Network, Lung Disease.

Sustainable smart system for vegetables plant disease detection: Four vegetable case studies

Agriculture is the backbone of the country’s economy. People depend on agriculture for food and exporting to generate income. However, agriculture faces various diseases that affect the quantity and quality of vegetables. Therefore, it is important to propose a model for detecting vegetable diseases. This study proposed a sustainable smart system for vegetable disease detection and classification. This system detects early vegetable diseases in common vegetables such as tomato, potato, lettuce, and cucumber. The study employed deep learning (DL) models to detect and classify vegetable diseases. Convolutional neural networks (CNN) are a type of DL model used for image classification. This study utilizes CNN and other extensions, such as VGG16 and MobileNet, for plant image classification. Three DL models were trained on four datasets for tomato disease classification, potato disease classification, lettuce disease classification, and cucumber disease classification. The results show that the three models achieved 84.49% accuracy on the tomato disease dataset, 97.65% accuracy on the cucumber disease dataset, 97% accuracy on the potato disease dataset, and 99.9% accuracy on the lettuce disease dataset. The proposed system can assist farmers in the early detection of vegetable diseases before they spread, and it can enhance agriculture by improving both the quality and quantity of products.

UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE

Heart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease.

2024/00264 Harnessing Transdisciplinary Knowledge: Integrated Deep Learning Techniques for Accurate Tomato Leaf Disease Classification

The study proposes a transdisciplinary approach integrating knowledge from fields such ascomputer science, botany, and data science to classifying leaf diseases. We integrated two deep-learningmodels that combine the strengths of the Inception network and the ResNet architecture to address thechallenge of accurately classifying tomato leaf diseases. The Inception network’s ability to quickly pick upvisual features on multiple scales is used to pull out fine-grained details that are needed to tell the differencebetween small changes in the shape of tomato leaves and disease symptoms. The ResNet architecture isgood at learning deep representations and getting around the vanishing gradient problem. This lets themodel learn the high-level concepts and complicated connections between different tomato leaf diseasepatterns. The integration of these two powerful deep-learning techniques results in a robust and highlyperformant tomato leaf classification model. Extensive tests on a 10-class dataset of tomato leaves, with 9disease categories and 1 healthy class, show that the proposed model works better than others, with a testset accuracy of 98.07%. The findings of this research contribute to the advancement of automated andefficient tomato leaf disease detection systems, which can aid in the early identification and management oftomato diseases, leading to improved crop yields and quality.

Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.

ANFIS Fuzzy convolutional neural network model for leaf disease detection.

Leaf disease detection is critical in agriculture, as it directly impacts crop health, yield, and quality. Early and accurate detection of leaf diseases can prevent the spread of infections, reduce the need for chemical treatments, and minimize crop losses. This not only ensures food security but also supports sustainable farming practices. Effective leaf disease detection systems empower farmers with the knowledge to take timely actions, leading to healthier crops and more efficient resource management. In an era of increasing global food demand and environmental challenges, advanced leaf disease detection technologies are indispensable for modern agriculture. This study presents an innovative approach for detecting pepper bell leaf disease using an ANFIS Fuzzy convolutional neural network (CNN) integrated with local binary pattern (LBP) features. Experiments involve using the models without LBP, as well as, with LBP features. For both sets of experiments, the proposed ANFIS CNN model performs superbly. It shows an accuracy score of 0.8478 without using LBP features while its precision, recall, and F1 scores are 0.8959, 0.9045, and 0.8953, respectively. Incorporating LBP features, the proposed model achieved exceptional performance, with accuracy, precision, recall, and an F1 score of higher than 99%. Comprehensive comparisons with state-of-the-art techniques further highlight the superiority of the proposed method. Additionally, cross-validation was applied to ensure the robustness and reliability of the results. This approach demonstrates a significant advancement in agricultural disease detection, promising enhanced accuracy and efficiency in real-world applications.

Machine learning based system for early heart disease detection and classification using audio signal processing approach

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, accounting for approximately 17.9 million deaths annually. Traditional diagnostic methods, though useful, have limitations such as invasiveness, high cost, and delays in detecting early-stage heart conditions. This study presents a novel machine learning-based system for early heart disease detection using audio signal processing, specifically leveraging phonocardiogram (PCG) data. Features were extracted using Mel-Frequency Cepstral Coefficients (MFCCs), Delta MFCCs, and Delta-Delta MFCCs, followed by dimensionality reduction via Principal Component Analysis (PCA). Support Vector Machine (SVM) and XGBoost classifiers were used to analyze the extracted features, and their performance was optimized through an ensemble model using the Moth Flame Optimization (MFO) algorithm. The model was rigorously evaluated using accuracy, precision, recall, and F1-score metrics. The ensemble model achieved an accuracy of 99.13%, precision of 98.94%, recall of 95.05%, and an F1-score of 97.46%. The application of SMOTE for data augmentation significantly improved classification performance, highlighting its effectiveness in addressing class imbalance. The proposed system provides a non-invasive, cost-effective solution for heart disease detection and holds potential for improving diagnostic access, particularly in resource-limited settings.

Plant Disease Detection and Classification

The Plant Disease Detection and classification project, aims to develop an automated system using machine learning and deep learning techniques to detect and classify plant diseases early on by analyzing leaf images. Early disease detection is crucial for increasing crop yields, reducing pesticide use, and promoting sustainable farming practices. The system will provide detailed descriptions of detected diseases along with recommended prevention and treatment methods. By using advanced machine learning and deep learning algorithms and data augmentation techniques, the system aims to improve the accuracy and efficiency of disease detection. While existing research shows promise, challenges like dataset robustness and environmental variability remain. This project addresses these challenges by integrating diverse datasets and leveraging sophisticated machine learning algorithms. The ultimate goal is to provide farmers with a reliable tool for managing plant health, ensuring food security, and contributing to sustainable agricultural practices. This will help farmers enhance resilience in the face of increasing agricultural challenges. Key Words: Plant Disease Detection, Automated Disease Detection System, Machine Learning, Deep Learning, Data Augmentation, Data Normalization, Support Vector Machine, KNN Classifier, Convolutional Neural Network, Decision Trees, Random Forest.

Implementing Real-Time Image Processing for Radish Disease Detection Using Hybrid Attention Mechanisms.

This paper developed a radish disease detection system based on a hybrid attention mechanism, significantly enhancing the precision and real-time performance in identifying disease characteristics. By integrating spatial and channel attentions, this system demonstrated superior performance across numerous metrics, particularly achieving 93% precision and 91% accuracy in detecting radish virus disease, outperforming existing technologies. Additionally, the introduction of the hybrid attention mechanism proved its superiority in ablation experiments, showing higher performance compared to standard self-attention and the convolutional block attention module. The study also introduced a hybrid loss function that combines cross-entropy loss and Dice loss, effectively addressing the issue of class imbalance and further enhancing the detection capability for rare diseases. These experimental results not only validate the effectiveness of the proposed method, but also provide robust technical support for the rapid and accurate detection of radish diseases, demonstrating its vast potential in agricultural applications. Future research will continue to optimize the model structure and computational efficiency to accommodate a broader range of agricultural disease detection needs.

Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier

Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists’ evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on.