A Smartphone based Automated Primary Screening of Oral Cancer based on Deep Learning DOI Creative Commons

Rinkal Shah,

Jyoti Pareek

INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

In low- and middle-income countries, oral cancer is becoming more common. One factor delaying the discovery of in rural areas a lack resources. To stop disease from spreading, it essential to quickly obtain information about any cancers. Therefore, carry out early identification before spreads. Primary screening maintained this study. Furthermore, deep neural network-based automated methods were used produce complex patterns address challenging issue assessing infection. The goal work develop an Android application that uses network categorize photos into four groups: erythroplakia, leukoplakia, ulcer, normal mouth. Convolutional networks K-fold validation processes are study’s methodology create customized Deep Oral Augmented Model (DOAM). Data augmentation techniques including shearing, scaling, rotation, flipping pre-process images. A convolutional then extract features images Optimal configurations max pooling layers, dropout, activation functions have resulted attainment maximum accuracies. By using ”ELU” function conjunction with RMSProp as optimizer, model achieves 96% accuracy, precision, F1 score, 68% testing accuracy. deployed TensorFlow Lite application.

Language: Английский

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions DOI Creative Commons
Tuan D. Pham, Muy‐Teck Teh,

Domniki Chatzopoulou

et al.

Current Oncology, Journal Year: 2024, Volume and Issue: 31(9), P. 5255 - 5290

Published: Sept. 6, 2024

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning natural language processing, their applications HNC. The integration of with imaging techniques, genomics, electronic health records explored, emphasizing its role early detection, biomarker discovery, planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, real-time monitoring systems are poised to further advance field. Addressing these fostering among experts, clinicians, researchers crucial developing equitable effective applications. future HNC holds significant promise, offering potential breakthroughs diagnostics, personalized therapies, improved patient outcomes.

Language: Английский

Citations

9
Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications DOI Creative Commons
Vineet Vinay, Praveen Jodalli, Mahesh Chavan

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 280 - 280

Published: Jan. 24, 2025

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral diagnosis applications address a gap. Methods: A review identified, selected, synthesized AI-based diagnosis, screening, prognosis literature. The verified study quality relevance using frameworks inclusion criteria. full search included keywords, MeSH phrases, Pubmed. AI were tested through data extraction synthesis. Results: outperforms traditional analysis, prediction approaches. Medical pictures can be used diagnose with convolutional neural networks. Smartphone AI-enabled telemedicine make screening affordable accessible in resource-constrained areas. methods predict risk patient data. also arrange treatment histopathology images heterogeneity, restricted longitudinal research, clinical practice inclusion, ethical legal difficulties. Future potential includes uniform standards, long-term investigations, regulatory frameworks, healthcare professional training. Conclusions: may transform treatment. It develop early detection, modelling, imaging phenotypic change, prognosis. approaches should standardized, longitudinally, practical issues related real-world deployment addressed.

Language: Английский

Citations

2
Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review DOI Creative Commons

Payam Mirfendereski,

Grace Y. Li,

Alexander T. Pearson

et al.

Frontiers in Oral Health, Journal Year: 2025, Volume and Issue: 6

Published: March 10, 2025

Oral cavity cancer is associated with high morbidity and mortality, particularly advanced stage diagnosis. cancer, typically squamous cell carcinoma (OSCC), often preceded by oral potentially malignant disorders (OPMDs), which comprise eleven variable risks for transformation. While OPMDs are clinical diagnoses, conventional exam followed biopsy histopathological analysis the gold standard diagnosis of OSCC. There vast heterogeneity in presentation OPMDs, possible visual similarities to early-stage OSCC or even various benign mucosal abnormalities. The diagnostic challenge OSCC/OPMDs compounded non-specialist primary care setting. has been significant research interest technology assist OSCC/OPMDs. Artificial intelligence (AI), enables machine performance human tasks, already shown promise several domains medical diagnostics. Computer vision, field AI dedicated data, over past decade applied photographs Various methodological concerns limitations may be encountered literature on OSCC/OPMD image analysis. This narrative review delineates current landscape photograph navigates limitations, issues, workflow implications this field, providing context future considerations.

Language: Английский

Citations

1
Interpretable Machine Learning for Oral Lesion Diagnosis Through Prototypical Instances Identification DOI

Alessio Cascione,

Mattia Setzu, Federico A. Galatolo

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 316 - 331

Published: Jan. 1, 2025

Language: Английский

Citations

0
Mathematical Foundations and Applications of Generative AI Models DOI

Naru Venkata Pavan Saish,

J. Jayashree,

J. Vijayashree

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A novel twin vision transformer framework for crop disease classification with deformable attention DOI

Smitha Padshetty,

Ambika

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107551 - 107551

Published: Feb. 7, 2025

Language: Английский

Citations

0
Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques DOI Creative Commons
B. N. Jagadesh, Srihari Varma Mantena,

Asha Prashant Sathe

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 15, 2025

This study presents a robust approach for continuous food recognition essential nutritional research, leveraging advanced computer vision techniques. The proposed method integrates Mutually Guided Image Filtering (MuGIF) to enhance dataset quality and minimize noise, followed by feature extraction using the Visual Geometry Group (VGG) architecture intricate visual analysis. A hybrid transformer model, combining Vision Transformer Swin variants, is introduced capitalize on their complementary strengths. Hyperparameter optimization performed Improved Discrete Bat Algorithm (IDBA), resulting in highly accurate efficient classification system. Experimental results highlight superior performance of achieving accuracy 99.83%, significantly outperforming existing methods. underscores potential architectures preprocessing techniques advancing systems, offering enhanced efficiency practical applications dietary monitoring personalized nutrition recommendations.

Language: Английский

Citations

0
Temporal focal modulation networks for sleep stage scoring DOI Creative Commons
Hasan Zan

Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)

Published: April 29, 2025

Language: Английский

Citations

0
Identification of benign and malignant breast nodules on ultrasound: comparison of multiple deep learning models and model interpretation DOI Creative Commons
Xi Wen,

Hao Tu,

Bingyang Zhao

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 18, 2025

Deep learning (DL) algorithms generally require full supervision of annotating the region interest (ROI), a process that is both labor-intensive and susceptible to bias. We aimed develop weakly supervised algorithm differentiate between benign malignant breast tumors in ultrasound images without image annotation. developed validated models using two publicly available datasets: (BUSI) GDPH&SYSUCC datasets. After removing poor quality images, total 3049 were included, divided into classes: (N = 1320 images) 1729 images). Weakly-supervised DL implemented with four networks (DenseNet121, ResNet50, EffientNetb0, Vision Transformer) trained 2136 unannotated images. 609 304 used for validation test sets, respectively. Diagnostic performances calculated as area under receiver operating characteristic curve (AUC). Using class activation map interpret prediction results algorithms. The DenseNet121 model, utilizing complete inputs ROI annotations, demonstrated superior diagnostic performance distinguishing nodules when compared EfficientNetb0, Transformer models. achieved highest AUC, values 0.94 on set 0.93 set, significantly surpassing other across datasets (all P < 0.05). model this study feasibility diagnosis tumor showed good capabilities differential diagnosis. This may help radiologists, especially novice doctors, improve accuracy ultrasound.

Language: Английский

Citations

0
Integrating Artificial Intelligence with Smartphone-based Imaging for Cancer Detection in vivo DOI
Bofan Song, Rongguang Liang

Biosensors and Bioelectronics, Journal Year: 2024, Volume and Issue: 271, P. 116982 - 116982

Published: Nov. 21, 2024

Language: Английский

Citations

2