A Comprehensive Survey of Deep Learning Approaches in Image Processing

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 531 - 531

Published: Jan. 17, 2025

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach traditional methodologies. This survey offers an in-depth exploration DL approaches that have redefined processing, tracing their evolution from early innovations to latest state-of-the-art developments. It also analyzes progression architectural designs and paradigms significantly enhanced ability process interpret complex visual data. Key such as techniques improving model efficiency, generalization, robustness, are examined, showcasing DL's address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous evaluation discussed, underscoring importance performance assessment in varied application contexts. impact is highlighted through its tackle challenges generate actionable insights. Finally, this identifies potential future directions, including emerging technologies like quantum computing neuromorphic architectures efficiency federated privacy-preserving training. Additionally, it highlights combining with edge explainable artificial intelligence (AI) scalability interpretability challenges. These advancements positioned further extend applications DL, driving innovation processing.

Language: Английский

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Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification

Applied Intelligence, Journal Year: 2022, Volume and Issue: 53(6), P. 7201 - 7215

Published: July 18, 2022

Language: Английский

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Understanding basic principles of Artificial Intelligence: a practical guide for intensivists.

PubMed, Journal Year: 2022, Volume and Issue: 93(5), P. e2022297 - e2022297

Published: Oct. 26, 2022

Artificial intelligence was born to allow computers learn and control their environment, trying imitate the human brain structure by simulating its biological evolution. makes it possible analyze large amounts of data (big data) in real-time, providing forecasts that can support clinician's decisions. This scenario include diagnosis, prognosis, treatment anesthesiology, intensive care medicine, pain medicine. Machine Learning is a subcategory AI. It based on algorithms trained for decisions making automatically recognize patterns from data. article aims offer an overview potential application AI anesthesiology analyzes operating principles machine learning Every pathway starts task definition ends model application.High-performance characteristics strict quality controls are needed during progress. During this process, different measures be identified (pre-processing, exploratory analysis, selection, processing evaluation). For inexperienced operators, process facilitated ad hoc tools engineering, learning, analytics.

Language: Английский

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Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems

Drones, Journal Year: 2022, Volume and Issue: 6(9), P. 222 - 222

Published: Aug. 26, 2022

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote complex accessing regions. The UAVs be utilized for application areas which hold sensitive data, necessitates secure processing using image encryption approaches. At the same time, embedded in latest technologies deep learning (DL) models monitoring such floods, collapsed buildings, fires faster mitigation its impacts on environment human population. This study develops an Artificial Intelligence-based Secure Communication Classification Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). proposed AISCC-DE2MS technique majorly employs classification situations. model follows a two-stage process: classification. initial stage, artificial gorilla troops optimizer (AGTO) algorithm ECC-Based ElGamal Encryption to accomplish security. For situation classification, encompasses densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, long short-term memory (LSTM)-based design AGTO-based optimal key generation PESO-based tuning demonstrate novelty our work. simulation analysis is tested AIDER dataset results performance terms different measures.

Language: Английский

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A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis

New Generation Computing, Journal Year: 2023, Volume and Issue: 41(2), P. 243 - 280

Published: March 16, 2023

Language: Английский

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Interpretable Classification of Pneumonia Infection Using eXplainable AI (XAI-ICP)

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 28896 - 28919

Published: Jan. 1, 2023

Open-box models in medical domain have high acceptance and demand by many examiners. Even though the accuracy predicted most of convolutional neural network (CNN) is high, it still not convincing as detail discussion regarding outcome semi-transparent functioning process. As pneumonia one top contagious infection that makes population affected due to low immunity. Therefore, goal this paper implement an interpretable classification using eXplainable AI (XAI-ICP). Thus, XAI-ICP highly efficient system designed solve challenge adapting recent health conditions. The aim design deep transfer learning based evaluation for classification. model primarily pre-trained open Chest X-Ray (CXR) dataset from National Institutes Health (NIH). Whereas, training input testing given Taichung Veterans General Hospital (TCVGH) independent learning, Taiwan + VinDr patients with labelled CXR images possessing three features infiltrate, cardiomegaly effusion. data labelling performed examiners XAI human-in-the-loop approach. demonstrates re-configurable DCNN a novel provides transparency analysis competitive accuracy. purpose work, can continuously improve itself feedback provide feasibility deployment across multiple countries then decisions taken at each step used within algorithm during hospitalization. scope be explainable usage diagnosis preprocessing evaluation. achieved 92.14% further improved on successive 93.29%. adapts different while providing results

Language: Английский

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Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones

Sensors, Journal Year: 2023, Volume and Issue: 23(11), P. 5148 - 5148

Published: May 28, 2023

Global warming and climate change are responsible for many disasters. Floods pose a serious risk require immediate management strategies optimal response times. Technology can respond in place of humans emergencies by providing information. As one these emerging artificial intelligence (AI) technologies, drones controlled their amended systems unmanned aerial vehicles (UAVs). In this study, we propose secure method flood detection Saudi Arabia using Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) classification model federated to minimize communication costs maximize global accuracy. We use blockchain-based partially homomorphic encryption (PHE) privacy protection stochastic gradient descent (SGD) share solutions. InterPlanetary File (IPFS) addresses issues with limited block storage posed high gradients information transmitted blockchains. addition enhancing security, FDSS prevent malicious users from compromising or altering data. Utilizing images IoT data, train local models that detect monitor floods. A technique is used encrypt each locally trained achieve ciphertext-level aggregation filtering, which ensures the be verified while maintaining privacy. The proposed enabled us estimate flooded areas track rapid changes dam water levels gauge threat. methodology straightforward, easily adaptable, offers recommendations Arabian decision-makers administrators address growing danger flooding. This study concludes discussion its challenges managing floods remote regions blockchain technology.

Language: Английский

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Intelligent waste classification approach based on improved multi-layered convolutional neural network

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(36), P. 84095 - 84120

Published: April 6, 2024

Abstract This study aims to improve the performance of organic recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating poor segregation schemes. Separating from can lead a faster more effective recycling process. Manual classification is time-consuming, costly, less accurate Automated in proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset 2 class category with 25077 images divided into 70% training 30% testing images. metrics used are Accuracy, Missed Detection Rate (MDR), False (FDR). results DCNN compared VGG16, VGG19, MobileNetV2, DenseNet121, EfficientNetB0 after transfer learning. Experimental show that image accuracy model reaches 93.28%.

Language: Английский

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Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: July 5, 2024

Language: Английский

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Balancing accuracy and efficiency: A lightweight deep learning model for COVID 19 detection

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108999 - 108999

Published: July 24, 2024

Language: Английский

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