Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging DOI Creative Commons
Ahmad A. Alzahrani, Jamal Alsamri, Mashael Maashi

et al.

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

Published: Feb. 24, 2025

Abstract Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed gradually significant public health concern in emerging low-to-middle-income states. Late diagnosis, high incidence, inadequate treatment strategies remain substantial challenges. Analysis at an initial phase for good treatment, prediction, existence. Despite current growth perception of molecular devices, late analysis methods near precision medicine OC patients challenge. A machine learning (ML) model was employed to improve early detection medicine, aiming reduce cancer-specific mortality disease progression. Recent advancements this approach have significantly enhanced extraction diagnosis critical information from medical images. This paper presents Deep Structured Learning with Vision Intelligence Carcinoma Lesion Segmentation Classification (DSLVI-OCLSC) imaging. Using imaging, DSLVI-OCLSC aims enhance OC’s classification recognition outcomes. To accomplish this, utilizes wiener filtering (WF) as pre-processing technique eliminate noise. In addition, ShuffleNetV2 method used group higher-level deep features input image. The convolutional bidirectional long short-term memory network multi-head attention mechanism (MA-CNN‐BiLSTM) utilized oral identification. Moreover, Unet3 + segment abnormal regions classified Finally, sine cosine algorithm (SCA) hyperparameter-tune DL model. wide range simulations implemented ensure performance under images dataset. experimental portrayed superior accuracy value 98.47% over recent approaches.

Language: Английский

A novel CNN-ViT-based deep learning model for early skin cancer diagnosis DOI
İshak Paçal, B. Özdemir, Javanshir Zeynalov

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107627 - 107627

Published: Jan. 28, 2025

Language: Английский

Citations

7
A robust deep learning framework for multiclass skin cancer classification DOI Creative Commons
Burhanettin Özdemir, İshak Paçal

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

Published: Feb. 10, 2025

Skin cancer represents a significant global health concern, where early and precise diagnosis plays pivotal role in improving treatment efficacy patient survival rates. Nonetheless, the inherent visual similarities between benign malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks separable self-attention mechanisms, tailored enhance feature extraction optimize classification performance. The inclusion of initial two stages is driven by their ability effectively capture fine-grained local features subtle patterns, which are critical for distinguishing visually similar lesion types. Meanwhile, adoption later allows selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing inefficiencies often associated with traditional mechanisms. was comprehensively trained validated on ISIC 2019 dataset, includes eight distinct skin categories. Advanced methodologies such as data augmentation transfer were employed further robustness reliability. proposed architecture achieved exceptional performance metrics, 93.48% accuracy, 93.24% precision, 90.70% recall, 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based Vision Transformer (ViT) models tested under comparable conditions. Despite its robust performance, maintains compact design only 21.92 million parameters, making it highly efficient suitable deployment. Proposed Model demonstrates accuracy generalizability across diverse classes, establishing reliable framework clinical practice.

Language: Английский

Citations

6
An Innovative Deep Learning Framework for Skin Cancer Detection Employing ConvNeXtV2 and Focal Self-Attention Mechanisms DOI Creative Commons
B. Özdemir, İshak Paçal

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103692 - 103692

Published: Dec. 1, 2024

Language: Английский

Citations

13
Explainable label guided lightweight network with axial transformer encoder for early detection of oral cancer DOI Creative Commons
Dhirendra Prasad Yadav, Bhisham Sharma, Ajit Noonia

et al.

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

Published: Feb. 21, 2025

Oral cavity cancer exhibits high morbidity and mortality rates. Therefore, it is essential to diagnose the disease at an early stage. Machine learning convolution neural networks (CNN) are powerful tools for diagnosing mouth oral cancer. In this study, we design a lightweight explainable network (LWENet) with label-guided attention (LGA) provide second opinion expert. The LWENet contains depth-wise separable layers reduce computation costs. Moreover, LGA module provides label consistency neighbor pixel improves spatial features. Furthermore, AMSA (axial multi-head self-attention) based ViT encoder incorporated in model global attention. Our (vision transformer) computationally efficient compared classical encoder. We tested LWRNet performance on MOD (mouth disease) OCI (oral image) datasets, results other CNN methods. achieved precision F1-scores of 96.97% 98.90% dataset, 99.48% 98.23% respectively. By incorporating Grad-CAM, visualize decision-making process, enhancing interpretability. This work demonstrates potential facilitating detection.

Language: Английский

Citations

0
Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging DOI Creative Commons
Ahmad A. Alzahrani, Jamal Alsamri, Mashael Maashi

et al.

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

Published: Feb. 24, 2025

Abstract Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed gradually significant public health concern in emerging low-to-middle-income states. Late diagnosis, high incidence, inadequate treatment strategies remain substantial challenges. Analysis at an initial phase for good treatment, prediction, existence. Despite current growth perception of molecular devices, late analysis methods near precision medicine OC patients challenge. A machine learning (ML) model was employed to improve early detection medicine, aiming reduce cancer-specific mortality disease progression. Recent advancements this approach have significantly enhanced extraction diagnosis critical information from medical images. This paper presents Deep Structured Learning with Vision Intelligence Carcinoma Lesion Segmentation Classification (DSLVI-OCLSC) imaging. Using imaging, DSLVI-OCLSC aims enhance OC’s classification recognition outcomes. To accomplish this, utilizes wiener filtering (WF) as pre-processing technique eliminate noise. In addition, ShuffleNetV2 method used group higher-level deep features input image. The convolutional bidirectional long short-term memory network multi-head attention mechanism (MA-CNN‐BiLSTM) utilized oral identification. Moreover, Unet3 + segment abnormal regions classified Finally, sine cosine algorithm (SCA) hyperparameter-tune DL model. wide range simulations implemented ensure performance under images dataset. experimental portrayed superior accuracy value 98.47% over recent approaches.

Language: Английский

Citations

0