A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features [PDF]
Scientific ReportsAccurate automated classification of brain tumors from magnetic resonance imaging (MRI) is essential for early diagnosis and treatment. This study presents a hybrid framework combining Convolutional Neural Network (CNN) deep features, Large Margin ...
Aynur Yonar
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IEEE Access, 2021 Deep neural networks (DNNs) have been successfully deployed in widespread domains, including healthcare applications. DenseNet201 is a new DNN architecture used in healthcare systems (i.e., presence detection of the surgical tool).
Khalid Adam Ismail Hammad +2 more
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Lesion detection using artificial intelligence models in MR images of prostate cancer and prostatitis patients and comparison of model performance [PDF]
Frontiers in UrologyAimThe diagnosis of prostate cancer and prostatitis becomes challenging when using biparametric Magnetic Resonance (MR) images. This research investigates deep learning models to assess their capability for improving diagnostic accuracy and assisting ...
Muhammed Kaya +4 more
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Multi-scale feature pyramid network with bidirectional attention for efficient mural image classification. [PDF]
PLoS ONEMural image recognition plays a critical role in the digital preservation of cultural heritage; however, it faces cross-cultural and multi-period style generalization challenges, compounded by limited sample sizes and intricate details, such as losses ...
Shulan Wang +3 more
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Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade [PDF]
DiagnosticsBackground: Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. Methods: This study aims to automate the assessment of KOA severity by training
Joo Chan Choi +3 more
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PLDC-Net: A Domain-Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks. [PDF]
Plant DirectABSTRACT Plant diseases are the cause of heavy losses of crop production and, therefore, a big contributor to food shortages. Identifying these diseases as early as possible is important to limit the negative effects that these diseases have on the yields, as slow response time will lead to the spread of diseases and further loss.
Richter DJ, Kim K.
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Effectiveness of Automatic Detection of Osteoarthritis using Convolutional Neural Network (CNN) Method with DenseNet201 on Digital Images of Knee Joint Radiography [PDF]
E3S Web of Conferences, 2023 The manual detection of osteoarthritis using Kellgren Lawrence system depends on experience and agreement between doctors. The study was conducted to develop DenseNet201 to assist doctors in making a diagnosis of osteoarthritis grading.
Nurfadhillah Dea +5 more
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Optimization of Vehicle Object Detection Based on UAV Dataset: CNN Model and Darknet Algorithm
JOIV: International Journal on Informatics Visualization, 2023 This study was conducted to identify several types of vehicles taken using drone technology or Unmanned Aerial Vehicles (UAV). The introduction of vehicles from above an altitude of more than 300-400 meters that pass the highway above ground level ...
Abdul Haris Rangkuti, Varyl Hasbi Athala
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International Journal of Electronics and Communications System, 2022 The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant.
Peshraw Ahmed Abdalla +5 more
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Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images
Diagnostics, 2023 Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in ...
Zhi-Hui Xu +5 more
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