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Aydin Y. A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors. Diagnostics (Basel) 2023; 13:3142. [PMID: 37835884 PMCID: PMC10572674 DOI: 10.3390/diagnostics13193142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023] Open
Abstract
Among the most serious types of cancer is skin cancer. Despite the risk of death, when caught early, the rate of survival is greater than 95%. This inspires researchers to explore methods that allow for the early detection of skin cancer that could save millions of lives. The ability to detect the early signs of skin cancer has become more urgent in light of the rising number of illnesses, the high death rate, and costly healthcare treatments. Given the gravity of these issues, experts have created a number of existing approaches for detecting skin cancer. Identifying skin cancer and whether it is benign or malignant involves detecting features of the lesions such as size, form, symmetry, color, etc. The aim of this study is to determine the most successful skin cancer detection methods by comparing the outcomes and effectiveness of the various applications that categorize benign and malignant forms of skin cancer. Descriptors such as the Local Binary Pattern (LBP), the Local Directional Number Pattern (LDN), the Pyramid of Histogram of Oriented Gradients (PHOG), the Local Directional Pattern (LDiP), and Monogenic Binary Coding (MBC) are used to extract the necessary features. Support vector machines (SVM) and XGBoost are used in the classification process. In addition, this study uses colored histogram-based features to classify the various characteristics obtained from the color images. In the experimental results, the applications implemented with the proposed color histogram-based features were observed to be more successful. Under the proposed method (the colored LDN feature obtained using the YCbCr color space with the XGBoost classifier), a 90% accuracy rate was achieved on Dataset 1, which was obtained from the Kaggle website. For the HAM10000 data set, an accuracy rate of 96.50% was achieved under a similar proposed method (the colored MBC feature obtained using the HSV color space with the XGBoost classifier).
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Affiliation(s)
- Yildiz Aydin
- Department of Computer Engineering, Erzincan Binali Yildirim University, Erzincan 24000, Turkey
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2
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Ghaheri P, Nasiri H, Shateri A, Homafar A. Diagnosis of Parkinson's disease based on voice signals using SHAP and hard voting ensemble method. Comput Methods Biomech Biomed Engin 2023:1-17. [PMID: 37771234 DOI: 10.1080/10255842.2023.2263125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/17/2023] [Indexed: 09/30/2023]
Abstract
Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's. The significant number of individuals afflicted with this illness makes it essential to develop a method to diagnose the conditions in their early phases. PD is typically identified from motor symptoms or via other Neuroimaging techniques. Expensive, time-consuming, and unavailable to the general public, these methods are not very accurate. Another issue to be addressed is the black-box nature of machine learning methods that needs interpretation. These issues encourage us to develop a novel technique using Shapley additive explanations (SHAP) and Hard Voting Ensemble Method based on voice signals to diagnose PD more accurately. Another purpose of this study is to interpret the output of the model and determine the most important features in diagnosing PD. The present article uses Pearson Correlation Coefficients to understand the relationship between input features and the output. Input features with high correlation are selected and then classified by the Extreme Gradient Boosting, Light Gradient Boosting Machine, Gradient Boosting, and Bagging. Moreover, the weights in Hard Voting Ensemble Method are determined based on the performance of the mentioned classifiers. At the final stage, it uses SHAP to determine the most important features in PD diagnosis. The effectiveness of the proposed method is validated using 'Parkinson Dataset with Replicated Acoustic Features' from the UCI machine learning repository. It has achieved an accuracy of 85.42%. The findings demonstrate that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases.
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Affiliation(s)
- Paria Ghaheri
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
| | - Hamid Nasiri
- Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Ahmadreza Shateri
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
| | - Arman Homafar
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
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Lou L, Liang H, Wang Z. Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device. Diagnostics (Basel) 2023; 13:diagnostics13071329. [PMID: 37046553 PMCID: PMC10093656 DOI: 10.3390/diagnostics13071329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/07/2023] Open
Abstract
The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers.
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Affiliation(s)
- Lu Lou
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
| | - Hong Liang
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou 570100, China
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Irmici G, Cè M, Caloro E, Khenkina N, Della Pepa G, Ascenti V, Martinenghi C, Papa S, Oliva G, Cellina M. Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? Diagnostics (Basel) 2023; 13:diagnostics13020216. [PMID: 36673027 PMCID: PMC9858224 DOI: 10.3390/diagnostics13020216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
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Affiliation(s)
- Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natallia Khenkina
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Giancarlo Oliva
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
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Brum AA, Duarte-Filho GC, Ospina R, Almeida FAG, Macêdo AMS, Vasconcelos GL. ModInterv: An automated online software for modeling epidemics. SOFTWARE IMPACTS 2022; 14:100409. [PMID: 35990010 PMCID: PMC9375249 DOI: 10.1016/j.simpa.2022.100409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/03/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic has proven the importance of mathematical tools to understand the evolution of epidemic outbreaks and provide reliable information to the general public and health authorities. In this perspective, we have developed ModInterv, an online software that applies growth models to monitor the evolution of the COVID-19 epidemic in locations chosen by the user among countries worldwide or states and cities in the USA or Brazil. This paper describes the software capabilities and its use both in recent research works and by technical committees assisting government authorities. Possible applications to other epidemics are also briefly discussed.
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Affiliation(s)
- Arthur A Brum
- Departamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, Pernambuco, Brazil
| | - Gerson C Duarte-Filho
- Departamento de Física - Universidade Federal de Sergipe, 49100-000, São Cristóvão, Sergipe, Brazil
| | - Raydonal Ospina
- Departamento de Estatística, CASTLab, Universidade Federal de Pernambuco, 50740-540, Recife, Pernambuco, Brazil
| | - Francisco A G Almeida
- Departamento de Física - Universidade Federal de Sergipe, 49100-000, São Cristóvão, Sergipe, Brazil
| | - Antônio M S Macêdo
- Departamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, Pernambuco, Brazil
| | - Giovani L Vasconcelos
- Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil
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Fatahi R, Nasiri H, Homafar A, Khosravi R, Siavoshi H, Chehreh Chelgani S. Modeling operational cement rotary kiln variables with explainable artificial intelligence methods – a “conscious lab” development. PARTICULATE SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1080/02726351.2022.2135470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Rasoul Fatahi
- School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Nasiri
- Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Arman Homafar
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
| | - Rasoul Khosravi
- Department of Mining, Faculty of Engineering, Lorestan University, Khorramabad, Iran
| | - Hossein Siavoshi
- Department of Mining and Geological Engineering, University of Arizona, Tucson, USA
| | - Saeed Chehreh Chelgani
- Minerals and Metallurgical Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden
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Fatahi R, Nasiri H, Dadfar E, Chehreh Chelgani S. Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach. Sci Rep 2022; 12:7543. [PMID: 35534588 PMCID: PMC9085744 DOI: 10.1038/s41598-022-11429-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/25/2022] [Indexed: 11/30/2022] Open
Abstract
Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.
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