Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(9), P. 2450 - 2450

Published: May 6, 2023

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers stakeholders for estimating traits such as crop yield diseases. Early detection disease is essential to prevent possible losses on ultimately increasing benefits. However, accurate estimation requires modern data analysis techniques machine learning deep learning. This work aims review actual progress detection, with an emphasis using UAV-based remote sensing. First, we present importance different image-processing improving UAV imagery. Second, propose a taxonomy accumulate categorize existing works Third, analyze summarize performance various methods detection. Finally, underscore challenges, opportunities research directions sensing

Language: Английский

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

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Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

Cognitive Computation, Journal Year: 2023, Volume and Issue: 16(1), P. 1 - 44

Published: Nov. 13, 2023

Abstract The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption the domain due typical blackbox nature these models. Therefore, facilitate among practitioners, models' predictions must explainable interpretable. emerging field (XAI) aims justify trustworthiness predictions. This work presents systematic review literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during last decade. Research questions carefully formulated categorise into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations SHAP, Gradient-weighted Class Activation Mapping GradCAM, Layer-wise Relevance Propagation LRP, XAI. categorisation provides broad coverage interpretation spectrum from intrinsic Ante-hoc models) complex patterns Post-hoc taking local explanations global scope. Additionally, forms interpretations providing in-depth insight factors support clinical diagnosis AD are also discussed. Finally, limitations, needs open challenges research outlined possible prospects their usage detection.

Language: Английский

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Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Systems, Journal Year: 2023, Volume and Issue: 11(2), P. 107 - 107

Published: Feb. 17, 2023

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across world. To slow spread COVID-19 virus, several measures have been adopted since start outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas smart cities requires modern technologies, such as deep learning transfer learning, computer vision automatic mask detection accurate control whether people wear correctly. This paper reviews progress research, emphasizing techniques. Existing datasets are first described discussed before presenting recent advances all related processing stages using a well-defined taxonomy, nature object detectors Convolutional Neural Network architectures employed their complexity, techniques that applied so far. Moving on, benchmarking results summarized, discussions regarding limitations methodologies provided. Last but least, future research directions detail.

Language: Английский

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A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BMC Medical Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 1, 2024

Abstract Background Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in world. Medical research has identified pneumonia, lung cancer, Corona Virus Disease 2019 (COVID-19) as prominent diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission (PET) others, primarily employed medical assessments because they provide computed data that can be utilized input datasets for computer-assisted diagnostic systems. used to develop evaluate machine learning (ML) methods analyze predict diseases. Objective This review analyzes ML paradigms, modalities' utilization, recent developments Furthermore, also explores various available publically being Methods The well-known databases academic studies have been subjected peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, many more, were search relevant articles. Applied keywords combinations procedures with primary considerations such COVID-19, ML, convolutional neural networks (CNNs), transfer learning, ensemble learning. Results finding indicates X-ray preferred detecting while CT scan predominantly favored cancer. COVID-19 detection, datasets. analysis reveals X-rays scans surpassed all other techniques. It observed using CNNs yields a high degree accuracy practicability identifying Transfer complementary techniques facilitate analysis. is metric assessment.

Language: Английский

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A novel framework for lung cancer classification using lightweight convolutional neural networks and ridge extreme learning machine model with SHapley Additive exPlanations (SHAP)

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 248, P. 123392 - 123392

Published: Feb. 7, 2024

Language: Английский

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A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 10, 2025

Brain tumors present a significant global health challenge, and their early detection accurate classification are crucial for effective treatment strategies. This study presents novel approach combining lightweight parallel depthwise separable convolutional neural network (PDSCNN) hybrid ridge regression extreme learning machine (RRELM) accurately classifying four types of brain (glioma, meningioma, no tumor, pituitary) based on MRI images. The proposed enhances the visibility clarity tumor features in images by employing contrast-limited adaptive histogram equalization (CLAHE). A PDSCNN is then employed to extract relevant tumor-specific patterns while minimizing computational complexity. RRELM model proposed, enhancing traditional ELM improved performance. framework compared with various state-of-the-art models terms accuracy, parameters, layer sizes. achieved remarkable average precision, recall, accuracy values 99.35%, 99.30%, 99.22%, respectively, through five-fold cross-validation. PDSCNN-RRELM outperformed pseudoinverse (PELM) exhibited superior introduction led enhancements performance parameters sizes those models. Additionally, interpretability was demonstrated using Shapley Additive Explanations (SHAP), providing insights into decision-making process increasing confidence real-world diagnosis.

Language: Английский

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An Automated Waste Classification System Using Deep Learning Techniques: Toward Efficient Waste Recycling and Environmental Sustainability

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113028 - 113028

Published: Jan. 1, 2025

Language: Английский

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PoxNet22: A Fine-Tuned Model for the Classification of Monkeypox Disease Using Transfer Learning

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 24053 - 24076

Published: Jan. 1, 2023

Officials in the field of public health are concerned about a new monkeypox outbreak, even though world is now experiencing an epidemic COVID-19. Similar to variola, cowpox, and vaccinia, caused by orthopoxvirus that has two strands double-stranded. The present pandemic been propagated sexually on massive scale, particularly among individuals who identify as gay or bisexual. In this particular instance, speed with which was diagnosed single most important aspect. It possible technology machine learning could be significant assistance accurately diagnosing sickness before it can spread more people. This study's goal determine solution problem developing model for diagnosis through application image processing methods. To accomplish this, data augmentation approaches have applied avoid chances model's overfitting, then transfer-learning strategy utilized apply preprocessed dataset total six different Deep Learning (DL) models. best precision, recall, accuracy performance matrices were selected after those three metrics compared one another. A called "PoxNet22" proposed performing fine-tuning performed best. PoxNet22 outperforms other methods its classification monkeypox, does 100% accuracy. outcomes study will prove extremely helpful clinicians process classifying sickness.

Language: Английский

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Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(5), P. 3125 - 3125

Published: Feb. 28, 2023

Kidney abnormality is one of the major concerns in modern society, and it affects millions people around world. To diagnose different abnormalities human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, used, which creates cross-sectional slices kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification segmentation purposes. However, has difficult clinicians interpret model’s specific decisions and, thus, creating “black box” system. Additionally, integrate complex internet-of-medical-things devices due demanding training parameters memory-resource cost. overcome these issues, this study proposed (1) lightweight customized convolutional neural network detect kidney cysts, stones, tumors (2) understandable AI Shapely values based on Shapley additive explanation predictive results local interpretable model-agnostic explanations illustrate model. The CNN model performed better than other state-of-the-art methods obtained an accuracy 99.52 ± 0.84% K = 10-fold stratified sampling. With improved interpretive power, work provides with conclusive results.

Language: Английский

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