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

J — Multidisciplinary Scientific Journal, Год журнала: 2024, Номер 7(1), С. 48 - 71

Опубликована: Янв. 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.

Язык: 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, Год журнала: 2024, Номер 15

Опубликована: Май 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.

Язык: Английский

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

Electronics, Год журнала: 2024, Номер 13(11), С. 2057 - 2057

Опубликована: Май 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.

Язык: Английский

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Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Sustainability, Год журнала: 2023, Номер 15(15), С. 11681 - 11681

Опубликована: Июль 28, 2023

The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields the face of numerous challenges, including plant diseases. Tomato is a widely cultivated essential crop that particularly susceptible disease, resulting significant economic losses hindrances security. Recently, Artificial Intelligence (AI) emerged as promising tool for detecting classifying tomato leaf diseases with exceptional accuracy efficiency, empowering farmers take proactive measures prevent damage production loss. AI algorithms are capable processing vast amounts data objectively without human bias, making them potent even subtle variations traditional techniques might miss. This paper provides comprehensive overview most recent advancements disease classification using Machine Learning (ML) Deep (DL) techniques, an emphasis how these approaches can enhance effectiveness classification. Several ML DL models, convolutional neural networks (CNN), evaluated review highlights various features used acquisition well evaluation metrics employed assess performance models. Moreover, this emphasizes address limitations classification, leading improved more efficient management ultimately contributing concludes by outlining research proposing new directions field AI-assisted These insights will be value researchers professionals interested utilizing contribute sustainable (SDG-3).

Язык: Английский

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A ResNet50-DPA model for tomato leaf disease identification

Frontiers in Plant Science, Год журнала: 2023, Номер 14

Опубликована: Окт. 16, 2023

Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which method based on convolutional neural network effective. While it challenging capture key features or tends lose a large number when extracting image by applying this method, resulting in low accuracy identification. Therefore, ResNet50-DPA model proposed identify tomato paper. Firstly, an improved ResNet50 included model, replaces first layer convolution basic with cascaded atrous convolution, facilitating obtaining different scales. Secondly, dual-path attention (DPA) mechanism search features, where stochastic pooling employed eliminate influence non-maximum values, two convolutions one dimension are introduced replace MLP effectively reducing damage information. In addition, quickly accurately type disease, DPA module incorporated into residual obtain enhanced feature map, helps reduce economic losses. Finally, visualization results Grad-CAM presented show that can more improve interpretability meeting need precise diseases.

Язык: Английский

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A Transfer Learning-Based Framework: MobileNet-SVM for Efficient Tomato Leaf Disease Classification

Опубликована: Май 2, 2024

In the face of a burgeoning global population exceeding seven billion and dwindling agricultural land, plants remain pivotal for sustaining human civilization's food needs. However, plant health is threatened by various diseases, particularly leaf ailments like spots, bacterial infections, black spots. These afflictions, predominantly caused bacteria fungi, jeopardize crop yields. Timely disease detection imperative safeguarding productivity. This study introduces novel hybrid approach amalgamating MobileNet, transfer learning-based model, with SVM (Support Vector Machine) hinge loss. Leveraging MobileNet's pre-trained capabilities, features are extracted fed into an classifier to discern nine distinct types tomato diseases healthy leaves. Statistical analysis underscores efficacy this surpassing previous benchmarks. Notably, it achieves exceptional classification accuracy, precision, recall, AUC values, culminating in impressive overall accuracy 99.37%.

Язык: Английский

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MiniTomatoNet: a lightweight CNN for tomato leaf disease recognition on heterogeneous FPGA-SoC

The Journal of Supercomputing, Год журнала: 2024, Номер 80(15), С. 21837 - 21866

Опубликована: Июнь 17, 2024

Язык: 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, Год журнала: 2025, Номер 173(1)

Опубликована: Янв. 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.

Язык: Английский

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

Frontiers in Plant Science, Год журнала: 2025, Номер 15

Опубликована: Янв. 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.

Язык: Английский

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Algorithms for Plant Monitoring Applications: A Comprehensive Review

Algorithms, Год журнала: 2025, Номер 18(2), С. 84 - 84

Опубликована: Фев. 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.

Язык: Английский

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