The role of pathologists in the diagnosis of occupational lung diseases: an expert opinion of the European Society of Pathology Pulmonary Pathology Working Group DOI Creative Commons
Fiorella Calabrese,

M. Angeles Montero-Fernandez,

Izidor Kern

et al.

Virchows Archiv, Journal Year: 2024, Volume and Issue: 485(2), P. 173 - 195

Published: July 20, 2024

Abstract Occupational lung/thoracic diseases are a major global public health issue. They comprise diverse spectrum of conditions with complex pathology, most which arise following chronic heavy workplace exposures to various mineral dusts, metal fumes, or inhaled organic particulate reactions. Many occupational lung could become irreversible; thus accurate diagnosis is mandatory minimize dust exposure and consequently reduce damage the respiratory system. Lung biopsy usually required when history inconsistent imaging, in case unusual new exposures, unexpected malignancy, cases there claims for personal injury legal compensation. In this paper, we provide an overview frequent focus on pathological diagnosis. This paper that summarizes expert opinion from group European pathologists, together contributions other specialists who crucial management these diseases. Indeed, tight collaboration all involved workup as many misdiagnosed go unrecognized. document provides guide pathologists practice facilitate disease. The review article reports relevant topics discussed during educational course held by active members Pulmonary Pathology Working Group Society Pathology. was endorsed University Padova “winter school” (selected project call “Shaping World-class University” 2022).

Language: Английский

A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images DOI

Pushpendra Singh Sisodia,

Gaurav Ameta, Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(4), P. 2409 - 2429

Published: Jan. 3, 2023

Language: Английский

Citations

43
A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Genetic Disorders Using Various Gene Disorders DOI
Neelam Chaplot, Dhiraj Pandey, Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(5), P. 3301 - 3323

Published: March 25, 2023

Language: Английский

Citations

25
Foodborne Disease Symptoms, Diagnostics, and Predictions Using Artificial Intelligence-Based Learning Approaches: A Systematic Review DOI
Yogesh Kumar, Inderpreet Kaur, Shakti Mishra

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 25, 2023

Language: Английский

Citations

25
A Review on Prediction and Prognosis of the Prostate Cancer and Gleason Grading of Prostatic Carcinoma Using Deep Transfer Learning Based Approaches DOI
G. Prabu Kanna,

S J K Jagadeesh Kumar,

Pavithra Parthasarathi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(5), P. 3113 - 3132

Published: Feb. 23, 2023

Language: Английский

Citations

24
A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases DOI Open Access
Kavita Thakur, Manjot Kaur, Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4477 - 4497

Published: June 8, 2023

Language: Английский

Citations

24
Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer DOI Creative Commons
Yogesh Kumar,

Pertik Garg,

Manu Raj Moudgil

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 8, 2024

Abstract Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic is vital to saving lives. Deep learning models have uplifted the sector by providing promising results diagnosing, detecting, classifying diseases. This paper explores role deep techniques detecting various organisms. The research works on dataset consisting 34,298 samples parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, Trichomonad along with host cells like red blood white cells. These images are initially converted from RGB grayscale followed computation morphological features perimeter, height, area, width. Later, Otsu thresholding watershed applied differentiate foreground background create markers for identification interest. transfer VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, hybrid model, InceptionResNetV2, employed. parameters these fine-tuned using three optimizers: SGD, RMSprop, Adam. Experimental reveal when RMSprop applied, EfficientNetB0 achieve highest accuracy 99.1% loss 0.09. Similarly, SGD optimizer, InceptionV3 performs exceptionally well, achieving 99.91% 0.98. Finally, applying Adam InceptionResNetV2 excels, 99.96% 0.13, outperforming other optimizers. findings this signify coupled image processing methods generates highly efficient way detect classify

Language: Английский

Citations

13
Metaheuristic-based hyperparameter optimization for multi-disease detection and diagnosis in machine learning DOI
Jagandeep Singh, Jasminder Kaur Sandhu, Yogesh Kumar

et al.

Service Oriented Computing and Applications, Journal Year: 2024, Volume and Issue: 18(2), P. 163 - 182

Published: Jan. 23, 2024

Language: Английский

Citations

9
A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis DOI Creative Commons
Xiaomin Hou,

Z.-M. Wei,

Xuelu Jiang

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 10, 2025

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, characterized chronic inflammation progressive fibrosis, potentially leading to respiratory and/or heart failure. Workers exposed dust, such as coal miners, foundry workers, construction are at risk pneumoconiosis. This review synthesizes international national classifications, epidemiological characteristics, strategies for prevention, clinical manifestations, diagnosis, pathogenesis, treatment Current research on pathogenesis pneumoconiosis focuses influence autophagy, apoptosis, pyroptosis progression disease. In addition, factors lipopolysaccharide nicotine have been found play crucial roles development provides comprehensive summary most fundamental achievements with purpose indicating future direction its control. New technologies integrative omics, artificial intelligence, systemic administration mesenchymal stromal cells proved useful solving conundrum These directional studies will provide novel therapeutic targets

Language: Английский

Citations

1
A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series Forecasting DOI
Sudersan Behera, Sarat Chandra Nayak, A. V. S. Pavan Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4401 - 4448

Published: May 23, 2023

Language: Английский

Citations

18
A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Lifestyle Diseases DOI
Krishna Modi, Ishbir Singh, Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4733 - 4756

Published: June 24, 2023

Language: Английский

Citations

18