An amalgamation of deep neural networks optimized with Salp swarm algorithm for cervical cancer detection

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110106 - 110106

Опубликована: Янв. 28, 2025

Язык: Английский

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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(6), С. 3267 - 3301

Опубликована: Фев. 19, 2024

Язык: Английский

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An explainable artificial intelligence model for identifying local indicators and detecting lung disease from chest X-ray images

Healthcare Analytics, Год журнала: 2023, Номер 4, С. 100206 - 100206

Опубликована: Июнь 10, 2023

One of the primary responsibilities radiologists is to diagnose lung illness using chest X-ray images. The radiologist examines patchy infection in imaging and makes a rational decision based on their knowledge. Convolutional neural networks work incredibly well classifying identifying diseases from medical Despite being promising prediction technology with accuracy equivalent person, deep learning (DL) models typically lack explainability, crucial component for clinical deployment DL highly regulated healthcare sector. In this paper, we mimic radiologist's decision-making process by local discriminate regions image through weekly supervised deriving rules, explaining why method gives such results. This carried out three phases. Phase one train model classification problem predict disease. two critical training identified images regions. combines global features more patterns classify diseases. fusion have shown remarkable improvement getting 99.6 percent fewer epochs.

Язык: Английский

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COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep Features

Computation, Год журнала: 2024, Номер 12(4), С. 66 - 66

Опубликована: Март 30, 2024

This study investigates techniques for medical image classification, specifically focusing on COVID-19 scans obtained through computer tomography (CT). Firstly, handcrafted methods based feature engineering are explored due to their suitability training traditional machine learning (TML) classifiers (e.g., Support Vector Machine (SVM)) when faced with limited datasets. In this context, I comprehensively evaluate and compare 27 descriptor sets. More recently, deep (DL) models have successfully analyzed classified natural images. However, the scarcity of well-annotated images, particularly those related COVID-19, presents challenges DL from scratch. Consequently, leverage features extracted 12 pre-trained classification tasks. work a comprehensive comparative analysis between TML approaches in classification.

Язык: Английский

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Robust modified passive islanding detection for microgrids using mathematical morphology based dual algorithm

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 20, 2025

The integration of distributed generation (DG) in microgrids has brought the challenge islanding detection, where a portion grid operates independently due to disconnection from main utility. Traditional detection methods often struggle balance speed, reliability, and non-detection zones (NDZ). This paper presents novel modified passive strategy based on mathematical morphological filter (MMF) with sliding window method-based median (SWMBMF), designed address these challenges microgrid systems. measured noisy voltage signal at point common coupling (PCC)/DGs-terminal is initially estimated via SWMBMF by continuously monitoring signals minimal latency. Then, MMF utilized process compute residuals index (VRI). Moreover, VRI are compared pre-specified threshold setting detect conditions, also effectively distinguishes between normal conditions. Comprehensive MATLAB/Simulink 2023b simulations demonstrate robustness proposed under various scenarios disturbances, proving its effectiveness ensuring stability reliability operations. method demonstrates an impressive accuracy 99%, successfully identifying events within 5 ms. rapid enhances minimizes risks associated delayed detection. scheme negligible non zone

Язык: Английский

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SG-UNet: Hybrid self-guided transformer and U-Net fusion for CT image segmentation

Journal of Visual Communication and Image Representation, Год журнала: 2025, Номер unknown, С. 104416 - 104416

Опубликована: Фев. 1, 2025

Язык: Английский

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A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июнь 12, 2024

Язык: Английский

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Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 23, 2025

The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis COVID-19 cases. As imaging technologies have advanced, 3D CNNs emerged as a powerful tool for segmenting classifying in medical images. These demonstrated both high accuracy rapid capabilities, making them crucial effective diagnostics. This study offers thorough review various CNN algorithms, evaluating their efficacy across range modalities. systematically examines recent advancements methodologies. process involved comprehensive screening abstracts titles to ensure relevance, followed by meticulous selection research papers from academic repositories. evaluates these based on specific criteria provides detailed insights into network architectures algorithms used detection. reveals significant trends use segmentation classification. It highlights key findings, including diverse employed compared other diseases, which predominantly utilize encoder/decoder frameworks. an in-depth methods, discussing strengths, limitations, potential areas future research. reviewed total 60 published repositories, Springer Elsevier. this implications clinical diagnosis treatment strategies. Despite some efficiency underscore advancing image findings suggest that could significantly enhance management COVID-19, contributing improved healthcare outcomes.

Язык: Английский

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DICA-Net: optimizing chest X-ray classification with attention U-Net and pigeon local search

Network Modeling Analysis in Health Informatics and Bioinformatics, Год журнала: 2025, Номер 14(1)

Опубликована: Апрель 9, 2025

Язык: Английский

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Midwifery learning and forecasting: Predicting content demand with user-generated logs

Artificial Intelligence in Medicine, Год журнала: 2023, Номер 138, С. 102511 - 102511

Опубликована: Фев. 25, 2023

Every day, 800 women and 6,700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal newborn deaths. Data science models together with logs generated by users online learning applications for midwives help improve their competencies. The goal is use rich behavioral data push digital towards personalized content provide an adaptive journey. In this work, we evaluate various forecasting methods determine the interest future on different kind contents available in app, broken down profession region.

Язык: Английский

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