An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

J — Multidisciplinary Scientific Journal, Journal Year: 2024, Volume and Issue: 7(1), P. 48 - 71

Published: Jan. 22, 2024

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity recent years shown promising results automated medical image analysis, particularly field chest radiology. This paper presents novel DL framework specifically designed for multi-class diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, COVID-19 using images, aiming address need efficient accessible diagnostic tools. The employs convolutional neural network (CNN) architecture with custom blocks enhance feature maps learn discriminative features from images. proposed is evaluated on large-scale dataset, demonstrating superior performance lung. In order evaluate effectiveness presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, specificity improvements. findings study showcased remarkable achieving 98.88%. metrics precision, recall, F1-score, Area Under Curve (AUC) averaged 0.9870, 0.9904, 0.9887, 0.9939 across six-class categorization system. research contributes provides foundation future advancements DL-based systems diseases.

Language: Английский

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Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: May 17, 2024

Tomato is one of the most popular and important food crops consumed globally. The quality quantity yield by tomato plants are affected impact made various kinds diseases. Therefore, it essential to identify these diseases early so that possible reduce occurrences effect on improve overall crop support farmers. In past, many research works have been carried out applying machine learning techniques segment classify leaf images. However, existing learning-based classifiers not able detect new types more accurately. On other hand, deep with swarm intelligence-based optimization enhance classification accuracy, leading effective accurate detection This paper proposes a method for harnessing power an ensemble model in sample dataset plants, containing images pertaining nine different introduces exponential moving average function temporal constraints enhanced weighted gradient optimizer integrated into fine-tuned Visual Geometry Group-16 (VGG-16) Neural Architecture Search Network (NASNet) mobile training methods providing improved accuracy. used consists 10,000 categorized classes validating additional 1,000 reserved testing model. results analyzed thoroughly benchmarked performance metrics, thus proving proposed approach gives better terms loss, precision, recall, receiver operating characteristic curve, F1-score values 98.7%, 4%, 97.9%, 98.6%, 99.97%, respectively.

Language: Английский

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Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

Electronics, Journal Year: 2024, Volume and Issue: 13(11), P. 2057 - 2057

Published: May 24, 2024

The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics automobiles smart agriculture. However, notable limitation of the its tendency to generate paths that lack desired smoothness. In response this challenge, particularly agricultural operations, research endeavours enhance evaluation individual nodes within search procedure improve overall smoothness resultant path. So, mitigate inherent choppiness A-star-generated agriculture, work adopts novel approach. It introduces utilizing Bezier curves as postprocessing step, thus refining generated imparting their This instrumental real-world applications where continuous safe motion imperative. outcomes simulations conducted part study affirm efficiency proposed methodology. These results underscore capability enhanced technique construct smooth pathways. Furthermore, they demonstrate performance. are also well suited deployment rural conditions, navigating complex terrains with precision critical necessity.

Language: Английский

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Crop Disease Detection by Deep Joint Segmentation and Hybrid Classification Model: A CAD‐Based Agriculture Development System

Journal of Phytopathology, Journal Year: 2025, Volume and Issue: 173(1)

Published: Jan. 1, 2025

ABSTRACT Precise detection of crop disease at the early stage is a crucial task, which will reduce spreading by taking preventive measures. The main goal this research to propose hybrid classification system for detecting utilising Modified Deep Joint (MDJ) segmentation. diseases involves five stages. They are data acquisition, pre‐processing, segmentation, feature extraction and detection. In initial stage, image diverse crops gathered in acquisition phase. According work, we considering Apple corn with benchmark datasets. input subjected pre‐processing median filtering process. Subsequently, pre‐processed under goes segmentation process, where proposed work. From segmented image, features like shape, colour, texture‐based Improved Median Binary Pattern (IMBP)‐based extracted. Finally, extracted given identifying diseases. model includes Bidirectional Long Short‐Term Memory (Bi‐LSTM) Belief Network (DBN) classifiers. outcome both classifiers score, an improved score level fusion model, determines final results. performance evaluated over existing methods various metrics. At training 90%, scheme attained accuracy 0.965, while conventional achieved less rates.

Language: Английский

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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 21, 2025

Tomatoes are considered one of the most valuable vegetables around world due to their usage and minimal harvesting period. However, effective still remains a major issue because tomatoes easily susceptible weather conditions other types attacks. Thus, numerous research studies have been introduced based on deep learning models for efficient classification tomato leaf disease. single architecture does not provide best results limited computational ability complexity. this used Transductive Long Short-Term Memory (T-LSTM) with an attention mechanism. The mechanism in T-LSTM has focus various parts image sequence. exploits specific characteristics training instances make accurate predictions. This can involve leveraging relationships patterns observed within dataset. is transductive approach scaled dot product evaluates weights each step hidden state patches which helps classification. data was gathered from PlantVillage dataset pre-processing conducted resizing, color enhancement, augmentation. These outputs were then processed segmentation stage where U-Net applied. After segmentation, VGG-16 feature extraction done through proposed experimental outcome shows that classifier achieved accuracy 99.98% comparably better than existing convolutional neural network transfer IBSA-NET.

Language: Английский

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Algorithms for Plant Monitoring Applications: A Comprehensive Review

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 84 - 84

Published: Feb. 5, 2025

Many sciences exploit algorithms in a large variety of applications. In agronomy, amounts agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. this particular field, the number scientific papers has significantly increased recent years, triggered scientists using artificial intelligence, comprising deep learning and machine methods bots, to process crop, plant, leaf images. Moreover, many other examples can be found, with different applied plant diseases phenology. This paper reviews publications which have appeared past three analyzing used classifying agronomic aims crops applied. Starting from broad selection 6060 papers, we subsequently refined search, reducing 358 research articles 30 comprehensive reviews. By summarizing advantages applying analyses, propose guide farming practitioners, agronomists, researchers, policymakers regarding best practices, challenges, visions counteract effects climate change, promoting transition towards more sustainable, productive, cost-effective encouraging introduction smart technologies.

Language: Английский

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XLTLDisNet: A Novel and Lightweight Approach to Identify Tomato Leaf Diseases with Transparency

Heliyon, Journal Year: 2025, Volume and Issue: 11(4), P. e42575 - e42575

Published: Feb. 1, 2025

Agricultural productivity is essential for global economic development by ensuring food security, boosting incomes and supporting employment. It enhances stability, reduces poverty promotes sustainable growth, creating a robust foundation overall progress improved quality of life worldwide. However, crop diseases can significantly affect agricultural output resources. The early detection these to minimize losses maximize production. In this study, novel Deep Learning (DL) model called Explainable Lightweight Tomato Leaf Disease Network (XLTLDisNet) has been proposed. proposed trained evaluated using publicly available PlantVillage tomato leaf disease dataset containing ten classes including healthy images. By leveraging different data augmentation techniques, the approach achieved an impressive accuracy 97.24%, precision 97.20%, recall 96.70% F1-score 97.10%. Additionally, explainable AI techniques such as Gradient-weighted Class Activation Mapping (GRAD-CAM) Local Interpretable Model-agnostic Explanations (LIME) have integrated into enhance explainability interpretability study.

Language: Английский

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CustomBottleneck-VGGNet: Advanced tomato leaf disease identification for sustainable agriculture

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110066 - 110066

Published: Feb. 11, 2025

Language: Английский

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Deep learning-based classification, detection, and segmentation of tomato leaf diseases: A state-of-the-art review

Artificial Intelligence in Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 192 - 220

Published: Feb. 20, 2025

Language: Английский

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On construction of data preprocessing for real-life SoyLeaf dataset & disease identification using Deep Learning Models

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 117, P. 108417 - 108417

Published: March 8, 2025

Language: Английский

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