A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

Lubbad, Mohammed A. H.; Kurtulus, Ikbal Leblebicioglu; Karaboga, Dervis; Kilic, Kerem; Basturk, Alper; Akay, Bahriye; Nalbantoglu, Ozkan Ufuk; Yilmaz, Ozden Melis Durmaz; Ayata, Mustafa; Yilmaz, Serkan; Pacal, Ishak

doi:10.1007/s10278-024-01086-x

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The models undergo a validation process, where more than 20 deep learning architectures and a total of 45 deep learning models are evaluated for classification. Finally, in the Evaluation stage, models are put to the test on the test data and their performance is evaluated and visually analyzed with Grad-CAM heatmaps [22].…”

Section: Methodsmentioning

confidence: 99%

“…In CAD applications, deep learning algorithms, notably Convolutional Neural Networks (CNNs), represent a significant leap forward compared to traditional machine learning methods [22]. These conventional techniques often rely on manually crafted features, a process that is not only time-consuming but also heavily dependent on the expertise of domain specialists.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Pacal,

Celik,

Bayram

et al. 2024

Cluster Comput

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

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Pacal,

Celik,

Bayram

et al. 2024

Cluster Comput

Self Cite

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Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP

Pacal,

Alaftekin,

Zengul

2024

J Digit Imaging. Inform. med.

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

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Artificial Intelligence in Reconstructive Implant Dentistry—Current Perspectives

Afrashtehfar,

Abuzayeda,

Murray

2024

Prosthesis

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A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

Cited by 3 publications

References 52 publications

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP

Artificial Intelligence in Reconstructive Implant Dentistry—Current Perspectives

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