Results 51 to 60 of about 5,255 (193)

Review on enhancing clinical decision support system using machine learning

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Clinical decision‐making is a complex patient‐centred process. For an informed clinical decision, the input data is very thorough ranging from detailed family history, environmental history, social history, health‐risk assessments, and prior relevant medical cases.
Anum Masood, Usman Naseem, Junaid Rashid, Jungeun Kim, Imran Razzak   +4 more
wiley   +1 more source

Artificial Intelligence in Periodontology: A Systematic Review

Journal of Periodontal Research, EarlyView.
AI shows promise across periodontology, with deep learning achieving strong performance for image‐based diagnosis of periodontitis. However, limited data diversity, inconsistent metrics, and scarce external validation raise concerns about generalizability and clinical applicability.
Antonin Tichy, Nils Werner, Helena Dujic, Charlotte Wetzel, Vinay Pitchika, Caspar Victor Bumm, Matthias Folwaczny, Falk Schwendicke   +7 more
wiley   +1 more source

Transformer‐Based Deep Learning Model for Predicting Recurrence in High‐Grade Glioma

Cancer Medicine, Volume 15, Issue 5, May 2026.
ABSTRACT Introduction The first year after treatment for high‐grade glioma (HGG) is recognized as the peak interval for recurrence. Accurate prediction of recurrence during this period is critical for timely management and early intervention. This study aimed to develop a fusion model that integrates MRI‐derived features with clinical variables ...
Xin Wang, Mingjun Ding, Dan Zong, Pudong Qian, Xia He   +4 more
wiley   +1 more source

Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms

Heliyon
Accurate detection of brain tumors is crucial for enhancing patient outcomes, yet the interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges. This study introduces a novel approach to brain tumor classification by exploring
Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel   +6 more
doaj   +1 more source

Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), 2023
Potato plants are plants that are beneficial to humans. Like other plants in general, potato plants also have diseases; if this disease is not treated immediately, there will be a significant decrease in food production. Therefore, it is necessary to detect diseases quickly and precisely so that disease control can be carried out effectively and ...
Charisma, Rifqi Alfinnur, Adhinata, Faisal Dharma   +1 more
openaire   +2 more sources

Machine Vision–Based Insect Recognition in Agriculture: A Comprehensive Review

Engineering Reports, Volume 8, Issue 4, April 2026.
This study explores machine vision for automated insect recognition in agriculture, leveraging deep learning and IoT integration. The review highlights real‐time pest detection methods, enhancing crop protection with minimal pesticide use. Comparative analysis of classifiers provides insights into performance and future advancements in sustainable ...
Mohammad Monirul Islam, Morium Akter, Mohammad Shorif Uddin, Md. Ataur Rahman, Md. Tarek Habib   +4 more
wiley   +1 more source

Classification of Cataract Disease with a DenseNet201 Based Deep Learning Model

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2022
Cataracts are among the most serious eye diseases and can cause blindness if left untreated. Since it is a treatable disease, professional knowledge of specialist ophthalmologists is needed. Ophthalmologists need to analyze images of the eye to detect clinical cataracts in an early stage.
Halit ÇETİNER, İbrahim ÇETİNER
openaire   +2 more sources

A Hybrid YOLO–SE Attention Based Deep Learning Framework for Robust Weapon Detection in Surveillance Imagery

Engineering Reports, Volume 8, Issue 4, April 2026.
This study presents a hybrid real‐time weapon detection system combining lightweight image classification (NASNetMobile) with precise object localization (YOLOv8n), optimizing performance and computational efficiency. The proposed pipeline enables scalable, accurate threat detection in surveillance systems, advancing security in resource‐constrained ...
Ashiful Nahar Bithi, Mridul Ghosh, Mrinal Kanti Saha Joy, Emtu Rani Paul, Md. Ferdous Bin Hafiz, Niaz Ashraf Khan, Shohag Barman, Md Mehedi Hasan, Hira Lal Gope, Md Masum Billah   +9 more
wiley   +1 more source

A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

, 2020
Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide ...
A Cruz-Roa, A Gupta, A Teramoto, B Korbar, C Deroulers, G Campanella, H Höfener, J Guan, J Jabalee, L Zhang, M Us-Krasovec, N Farahani, ND Narvekar, O Ronneberger, P Kainz, P Speight, R Ferzli, R Sankaranarayanan, S Warnakulasuriya, T Falk, W Xie, Y Xie   +21 more
core   +1 more source

Evaluating Transfer Learning Models for Vehicle Scene Classification Under Adverse Weather Classification Conditions

Engineering Reports, Volume 8, Issue 4, April 2026.
This work presents transfer learning models for robust vehicle identification across varying weather conditions. By leveraging deep learning techniques, the study enhances recognition accuracy, ensuring consistent performance in adverse environments. The findings contribute to intelligent transportation systems and automated surveillance applications ...
Agha Asim Husain, Tanmoy Maity, Gunjan Gupta, Mritunjay Rai, R. K. Yadav   +4 more
wiley   +1 more source