4.7 Article

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Chemistry, Analytical

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

K. Suresh Manic et al.

Summary: This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. The study verifies that the schizophrenia screening accuracy with DF+HF (deep features + handcrafted features) is superior compared with other methods. This framework is clinically significant and can be used to inspect actual patients' brain MRI slices in the future.

SENSORS (2023)

Article

Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network

Madhusmita Das et al.

International Journal of Environmental Research and Public Health (2023)

Article Public, Environmental & Occupational Health

A framework to distinguish healthy/cancer renal CT images using the fused deep features

Venkatesan Rajinikanth et al.

Summary: Cancer rates in the kidney are on the rise, and accurate detection and management are crucial. This study focuses on developing a framework to classify renal CT images using deep-learning schemes, with a pre-processing scheme to improve accuracy. The experimental results show that the KNN classifier achieves 100% detection accuracy with the pre-processed CT slices, making it clinically significant.

FRONTIERS IN PUBLIC HEALTH (2023)

Article Chemistry, Analytical

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Atta-ur Rahman et al.

Summary: Oral cancer is a dangerous and common cancer, traditional microscopic biopsy image detection methods have limitations, and there is a high possibility of human error. With the development of technology, deep learning algorithms play an increasingly important role in the field of oral cancer image diagnosis, improving classification accuracy.

SENSORS (2022)

Article Chemistry, Analytical

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

Monagi H. Alkinani et al.

Summary: This research proposes a hybrid scheme for detecting distracted driving, which combines handcrafted and deep CNN features. By fusing and selecting features, and using KNN and SVM classifiers for classification, the accuracy of detection is improved.

SENSORS (2022)

Article Mathematics

Utilization of Improved Machine Learning Method Based on Artificial Hummingbird Algorithm to Predict the Tribological Behavior of Cu-Al2O3 Nanocomposites Synthesized by In Situ Method

Ayman M. Sadoun et al.

Summary: This paper presents a machine learning model using the artificial hummingbird algorithm to predict the effect of Al2O3 nanoparticles content on the wear rates in Cu-Al2O3 nanocomposite. The experimental results show that the addition of Al2O3 nanoparticles can improve the microhardness of the composite and reduce its wear rate. The developed model is able to accurately predict the wear rates under different wear conditions.

MATHEMATICS (2022)

Article Computer Science, Artificial Intelligence

Comparison of Convolutional Neural Network for Classifying Lung Diseases from Chest CT Images

Ramya Mohan et al.

Summary: This paper proposes a convolutional neural network (MIDNet18) based on a customized Medical Image Analysis and Detection network for diagnosing various lung illnesses from chest CT images. The network demonstrates simplified model building, minimal complexity, easy technique, and high-performance accuracy in classifying binary and multiclass medical images. The performance of the MIDNet18 architecture is compared with existing models like LeNet-5, VGG-16, VGG-19, and ResNet-50, showing improved training and test accuracy.

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (2022)

Article Engineering, Multidisciplinary

An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems

Weiguo Zhao et al.

Summary: The multi-objective Artificial hummingbird algorithm (MOAHA) is developed to solve complex multi-objective optimization problems, including engineering design problems. The algorithm utilizes an external archive to save Pareto optimal solutions and maintains population diversity through a dynamic elimination-based crowding distance (DECD) method. Additionally, a non-dominated sorting strategy is merged with MOAHA to improve the convergence of the algorithm. The comprehensive tests demonstrate the superior performance of MOAHA over competitors in terms of convergence, diversity, and solution distribution. The algorithm is also shown to excel in handling challenging real-world multi-objective problems with unknown true Pareto optimal solutions and fronts.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2022)

Article Chemistry, Multidisciplinary

A Hybrid U-Lossian Deep Learning Network for Screening and Evaluating Parkinson's Disease

Rytis Maskeliunas et al.

Summary: Speech impairment analysis and processing technologies have evolved, and voice analysis as a biomarker for Parkinson's Disease has gained popularity. We have developed a deep learning-driven method for clinical speech signal processing, which shows promise as a screening tool. By recognizing and evaluating voice quality and deterioration, vocal digital biomarkers can supplement traditional manual examination and improve the efficiency of Parkinson's Disease diagnosis.

APPLIED SCIENCES-BASEL (2022)

Article Biology

Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm

Ramya Mohan et al.

Summary: The incidence rate of communicable diseases is increasing, and this study aims to develop an automatic tuberculosis (TB) detection system to assist pulmonologists in diagnosing and treating the disease. The system utilizes image processing and feature optimization techniques, achieving a classification accuracy of 98.6190% with the SVM-Medium Gaussian classifier.

LIFE-BASEL (2022)

Article Agronomy

Explainable Neural Network for Classification of Cotton Leaf Diseases

Javeria Amin et al.

Summary: Every nation's development relies on agriculture, with cotton and other important crops being referred to as cash crops. Cotton is susceptible to various diseases that affect crop yield, but early disease detection is crucial for crop protection. Computerized methods, involving feature extraction and classification, play a vital role in accurately detecting diseases in cotton crops.

AGRICULTURE-BASEL (2022)

Article Energy & Fuels

A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems

Ahmed Fathy

Summary: This study proposes a novel metaheuristic approach, artificial hummingbird algorithm (AHA), to determine the best locations and sizes of biomass-based distributed generators (DGs) in radial distribution networks. The approach optimizes the network active power loss and voltage deviation, achieving the effective integration of DGs in the network.

APPLIED ENERGY (2022)

Article Biology

Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features

Fan Zhang et al.

Summary: Preoperative assessment of tracheal intubation difficulty is crucial in anesthesia practice. Current AI methods for Mallampati classification are unreliable, relying solely on doctors' experience. This study proposes a new automatic Mallampati classification method that combines deep features and handcrafted features to improve the accuracy of difficulty assessment in tracheal intubation.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Assessment of the association of deep features with a polynomial algorithm for automated oral epithelial dysplasia grading

Adriano B. Silva et al.

Summary: This study presents a method for grading oral epithelial dysplasia in histopathological images using deep features and a polynomial classifier. The ResNet50 and AlexNet models were trained and information was extracted from the convolutional layers. The results showed that the proposed method provided relevant results in terms of accuracy and AUC values, serving as a tool to aid pathologists.

2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022) (2022)

Proceedings Paper Computer Science, Theory & Methods

Intelligence Combiner: A Combination of Deep Learning and Handcrafted Features for an Adolescent Psychosis Prediction using EEG Signals

Ejay Nsugbe et al.

Summary: This study explores the fusion of automatically extracted features via convolutional neural networks (CNN) and handcrafted features to enhance the prediction of schizophrenia in adolescents. The experimental results demonstrated a 98% classification accuracy in predicting adolescent psychosis using the fused set of features, showcasing a superior intelligence comprising of both handcrafted and CNN features.

PROCEEDINGS OF 2022 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR INDUSTRY 4.0 & IOT (IEEE METROIND4.0&IOT) (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Detection of Oral Cavity Squamous Cell Carcinoma from Normal Epithelium of the Oral Cavity using Microscopic Images

Chiagoziem C. Ukwuoma et al.

Summary: This paper introduces an early detection method for Oral Cavity Squamous Cell Carcinoma (OCSCC) using deep learning models and ensemble pre-trained models for microscopic image analysis. The proposed approach demonstrates its robustness on both low-quality and high-quality images, achieving improvement compared to transfer learning methods. The results support the effectiveness of the proposed methodology in detecting and classifying OCSCC.

2022 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATIONS (DASA) (2022)

Review Biochemistry & Molecular Biology

Liquid Biopsy and Circulating Biomarkers for the Diagnosis of Precancerous and Cancerous Oral Lesions

Giuseppe Gattuso et al.

Summary: Liquid biopsy has emerged as a promising minimally invasive tool for the early detection and personalized treatment of oral cancer, by detecting circulating biomarkers. However, further studies are needed to clarify its clinical impact.

NON-CODING RNA (2022)

Article Oncology

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung et al.

Summary: The global cancer burden in 2020 saw an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths. Female breast cancer surpassed lung cancer as the most commonly diagnosed cancer, while lung cancer remained the leading cause of cancer death. These trends are expected to rise in 2040, with transitioning countries experiencing a larger increase compared to transitioned countries due to demographic changes and risk factors associated with globalization and a growing economy. Efforts to improve cancer prevention measures and provision of cancer care in transitioning countries will be crucial for global cancer control.

CA-A CANCER JOURNAL FOR CLINICIANS (2021)

Review Computer Science, Artificial Intelligence

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review

Rasheed Omobolaji Alabi et al.

Summary: Machine learning has shown promising potential in revolutionizing the diagnosis and prognosis of oral squamous cell carcinoma, but faces limitations and concerns in clinical implementation.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Multidisciplinary Sciences

Incidence trends of breast cancer in Saudi Arabia: A joinpoint regression analysis (2004-2016)

Salman M. Albeshan et al.

Summary: The study reported an increasing trend in breast cancer cases among Saudi females from 2004 to 2016, with encouraging outcomes including a shift towards older age at diagnosis and an increased proportion of early-stage diagnoses. Higher annual percent changes were observed in smaller regions, emphasizing the need for region-based studies.

JOURNAL OF KING SAUD UNIVERSITY SCIENCE (2021)

Article Multidisciplinary Sciences

Pneumonia detection in chest X-ray images using an ensemble of deep learning models

Rohit Kundu et al.

Summary: Pneumonia, a respiratory infection caused by bacteria or viruses, affects developing and underdeveloped nations more severely; early diagnosis is crucial, with chest X-ray imaging being the common method used.

PLOS ONE (2021)

Review Medicine, General & Internal

Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review

Sanjeev B. Khanagar et al.

Summary: This paper reports on the application and performance of artificial intelligence (AI) in diagnosing and predicting the occurrence of oral cancer (OC). The precision and accuracy of AI in diagnosis and predicting the occurrence of OC are higher than current clinical strategies and conventional statistical methods.

DIAGNOSTICS (2021)

Article Multidisciplinary Sciences

Histopathological imaging database for oral cancer analysis

Tabassum Yesmin Rahman et al.

DATA IN BRIEF (2020)

Article Computer Science, Artificial Intelligence

Global weighted LBP based entropy features for the assessment of pulmonary hypertension

Anjan Gudigar et al.

PATTERN RECOGNITION LETTERS (2019)

Proceedings Paper Computer Science, Information Systems

Multi-Model Deep Neural Network based Features Extraction and Optimal Selection Approach for Skin Lesion Classification

Muhammad Attique Khan et al.

2019 INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCES (ICCIS) (2019)

Article Microscopy

Textural pattern classification for oral squamous cell carcinoma

T. Y. Rahman et al.

JOURNAL OF MICROSCOPY (2018)

Article Engineering, Electrical & Electronic

Discrete wavelet transform based principal component averaging fusion for medical images

R. Vijayarajan et al.

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS (2015)

Review Computer Science, Artificial Intelligence

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Di Huang et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2011)

Article Computer Science, Artificial Intelligence

Description of interest regions with local binary patterns

Marko Heikkila et al.

PATTERN RECOGNITION (2009)