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

Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP DOI Creative Commons
İshak Paçal, Melek Alaftekin, Ferhat D. Zengul

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: June 5, 2024

Abstract Skin cancer is one of the most frequently occurring cancers worldwide, and early detection crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, strict time limits, which can negatively affect diagnostic outcomes. Deep learning–based systems offer quick, accurate testing enhanced research capabilities, providing significant support to dermatologists. In this study, we Swin Transformer architecture by implementing hybrid shifted window-based multi-head self-attention (HSW-MSA) in place conventional (SW-MSA). This adjustment enables model more efficiently process areas skin overlap, capture finer details, manage long-range dependencies, while maintaining memory usage computational efficiency during training. Additionally, study replaces standard multi-layer perceptron (MLP) with a SwiGLU-based MLP, an upgraded version gated linear unit (GLU) module, achieve higher accuracy, faster training speeds, better parameter efficiency. The modified model-base was evaluated using publicly accessible ISIC 2019 dataset eight classes compared against popular convolutional neural networks (CNNs) cutting-edge vision transformer (ViT) models. exhaustive assessment on unseen test dataset, proposed Swin-Base demonstrated exceptional performance, achieving accuracy 89.36%, recall 85.13%, precision 88.22%, F1-score 86.65%, surpassing all previously reported deep learning models documented literature.

Language: Английский

Citations

22
Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification DOI Creative Commons
İshak Paçal, Ömer Çelik, Bilal Bayram

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(8), P. 11187 - 11212

Published: May 20, 2024

Abstract The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination such conditions. Despite advancements Computer-Aided Diagnosis (CADx) systems powered by deep learning, challenge accurately classifying from MRI scans persists due to high variability tumor appearances subtlety early-stage manifestations. This work introduces novel adaptation EfficientNetv2 architecture, enhanced Global Attention Mechanism (GAM) Efficient Channel (ECA), aimed at overcoming these hurdles. enhancement not only amplifies model’s ability focus on salient features within complex images but also significantly improves classification accuracy tumors. Our approach distinguishes itself meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance detecting broad spectrum Demonstrated through extensive experiments large public dataset, our model achieves an exceptional high-test 99.76%, setting new benchmark MRI-based classification. Moreover, incorporation Grad-CAM visualization techniques sheds light decision-making process, offering transparent interpretable insights are invaluable clinical assessment. By addressing limitations inherent previous models, this study advances field medical imaging analysis highlights pivotal role enhancing interpretability learning models diagnosis. research sets stage advanced CADx systems, patient care outcomes.

Language: Английский

Citations

19
A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases DOI
Bilal Bayram, İsmail Kunduracıoğlu, Suat İnce

et al.

Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

13
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
Quantum computational infusion in extreme learning machines for early multi-cancer detection DOI Creative Commons
Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah

et al.

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Language: Английский

Citations

5
Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices DOI Creative Commons
Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(1), P. 62 - 62

Published: Jan. 13, 2025

The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight efficient RetinaNet variant tailored edge device deployment. model reduces computational overhead while maintaining high accuracy by replacing the computationally intensive ResNet backbone with MobileNet leveraging depthwise separable convolutions. modified achieves an average precision (AP) 32.1, surpassing state-of-the-art models small tumor (APS: 14.3) large localization (APL: 49.7). Furthermore, significantly costs, making real-time analysis feasible on low-power hardware. Clinical relevance key focus this work. proposed addresses diagnostic challenges small, variable-sized often overlooked existing methods. Its architecture enables portable devices, bridging gap accessibility underserved regions. Extensive experiments BRATS dataset demonstrate robustness across sizes configurations, confidence scores consistently exceeding 81%. advancement holds potential improving early detection, particularly remote areas lacking advanced infrastructure, thereby contributing to better patient outcomes broader AI-driven tools.

Language: Английский

Citations

2
Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images DOI
Prabhpreet Kaur, Priyanka Mahajan

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109790 - 109790

Published: Feb. 13, 2025

Language: Английский

Citations

2
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
Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model DOI Creative Commons
Moneerah Alotaibi, Amal Alshardan, Mashael Maashi

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 3, 2024

Cancer seems to have a vast number of deaths due its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories cancer that may affect males females occur worldwide are colon lung cancer. A precise on-time analysis this can increase the survival rate improve appropriate treatment characteristics. An efficient effective method speedy accurate recognition tumours in areas is provided as an alternative methods. Earlier diagnosis disease on front drastically reduces chance death. Machine learning (ML) deep (DL) approaches accelerate diagnosis, facilitating researcher workers study majority patients limited period at low cost. This research presents Histopathological Imaging Early Detection Lung Colon via Ensemble DL (HIELCC-EDL) model. HIELCC-EDL technique utilizes histopathological images identify (LCC). To achieve this, uses Wiener filtering (WF) noise elimination. In addition, model channel attention Residual Network (CA-ResNet50) complex feature patterns. Moreover, hyperparameter selection CA-ResNet50 performed using tuna swarm optimization (TSO) technique. Finally, detection LCC achieved by ensemble three classifiers such extreme machine (ELM), competitive neural networks (CNNs), long short-term memory (LSTM). illustrate promising performance model, complete set experimentations was benchmark dataset. experimental validation portrayed superior accuracy value 99.60% over recent approaches.

Language: Английский

Citations

5