MobileTurkerNeXt: investigating the detection of Bankart and SLAP lesions using magnetic resonance images
Article
Gurger, Murat, Esmez, Omer, Key, Sefa, Hafeez-Baig, Abdul, Dogan, Sengul and Tuncer, Turker. 2025. "MobileTurkerNeXt: investigating the detection of Bankart and SLAP lesions using magnetic resonance images." Radiological Physics and Technology. https://doi.org/10.1007/s12194-025-00918-x
Article Title | MobileTurkerNeXt: investigating the detection of Bankart and SLAP lesions using magnetic resonance images |
---|---|
ERA Journal ID | 214038 |
Article Category | Article |
Authors | Gurger, Murat, Esmez, Omer, Key, Sefa, Hafeez-Baig, Abdul, Dogan, Sengul and Tuncer, Turker |
Journal Title | Radiological Physics and Technology |
Number of Pages | 17 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Japan |
ISSN | 1865-0333 |
1865-0341 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s12194-025-00918-x |
Web Address (URL) | https://link.springer.com/article/10.1007/s12194-025-00918-x |
Abstract | The landscape of computer vision is predominantly shaped by two groundbreaking methodologies: transformers and convolutional neural networks (CNNs). In this study, we aim to introduce an innovative mobile CNN architecture designed for orthopedic imaging that efficiently identifies both Bankart and SLAP lesions. Our approach involved the collection of two distinct magnetic resonance (MR) image datasets, with the primary goal of automating the detection of Bankart and SLAP lesions. A novel mobile CNN, dubbed MobileTurkerNeXt, forms the cornerstone of this research. This newly developed model, comprising roughly 1 million trainable parameters, unfolds across four principal stages: the stem, main, downsampling, and output phases. The stem phase incorporates three convolutional layers to initiate feature extraction. In the main phase, we introduce an innovative block, drawing inspiration from ConvNeXt, EfficientNet, and ResNet architectures. The downsampling phase utilizes patchify average pooling and pixel-wise convolution to effectively reduce spatial dimensions, while the output phase is meticulously engineered to yield classification outcomes. Our experimentation with MobileTurkerNeXt spanned three comparative scenarios: Bankart versus normal, SLAP versus normal, and a tripartite comparison of Bankart, SLAP, and normal cases. The model demonstrated exemplary performance, achieving test classification accuracies exceeding 96% across these scenarios. The empirical results underscore the MobileTurkerNeXt's superior classification process in differentiating among Bankart, SLAP, and normal conditions in orthopedic imaging. This underscores the potential of our proposed mobile CNN in advancing diagnostic capabilities and contributing significantly to the field of medical image analysis. |
Keywords | Bankart detection; MobileTurkerNeXt ; SLAP detection; CNN ; Biomedical image classifcation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4601. Applied computing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Business |
School of Health and Medical Sciences | |
Centre for Health Research |
Permalink -
https://research.usq.edu.au/item/zy91q/mobileturkernext-investigating-the-detection-of-bankart-and-slap-lesions-using-magnetic-resonance-images
2
total views0
total downloads2
views this month0
downloads this month
Export as
Related outputs
Dr Abdul Hafeez-Baig
Hafeez-Baig, A.. Dr Abdul Hafeez-Baig.Spectrogram-based Deep Learning Approach for Anomaly Detection from Cough Sounds
Keles, Tugce, Dogan, Sengul, Hafeez-Baig, Abdul and Tuncer, Turker. 2025. "Spectrogram-based Deep Learning Approach for Anomaly Detection from Cough Sounds." International Journal of Information Technology and Computer Science (IJITCS). 17 (3), pp. 1-12. https://doi.org/10.5815/ijitcs.2025.03.01MuRAt-CAP-Net: A novel multi-input residual attention network for automated detection of A-phases and subtypes in cyclic alternating patterns
Yaman, Suleyman, Güler, Hasan, Sengur, Abdulkadir, Hafeez-Baig, Abdul and Acharya, U. Rajendra. 2025. "MuRAt-CAP-Net: A novel multi-input residual attention network for automated detection of A-phases and subtypes in cyclic alternating patterns." Biomedical Signal Processing and Control. 110 (Part A). https://doi.org/10.1016/j.bspc.2025.108221AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction
Chadalavada, Sreeni, Yaman, Suleyman, Sengur, Abdulkadir, Deo, Ravinesh C., Hafeez-Baig, Abdul, Kolbe-Alexander, Tracy, Sampathila, Niranjana and Acharya, U. Rajendra. 2025. "AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction." IEEE Access. 13, pp. 96261-96276. https://doi.org/10.1109/ACCESS.2025.3574722Gated-LNN: Gated Liquid Neural Networks for Accurate Water Quality Index Prediction and Classification
Chadalavada, Sreeni, Yaman, Süleyman, Yaman, Suleyman, Sengur, Abdulkadi, Hafeez-Baig, Abdul, Tan, Ru-San, Barua, Prabal Datta, Deo, Ravinesh C., Kobayashi, Makiko and Acharya, U. Rajendra. 2025. "Gated-LNN: Gated Liquid Neural Networks for Accurate Water Quality Index Prediction and Classification." IEEE Access. 13, pp. 69500-69512. https://doi.org/10.1109/ACCESS.2025.3561593StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images
Ekingen, Evren, Yildirim, Ferhat, Bayar, Ozgur, Akbal, Erhan, Sercek, Ilknur, Hafeez-Baig, Abdul, Dogan, Sengul and Tuncer, Turker. 2025. "StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images." BMC Medical Imaging. 25 (1). https://doi.org/10.1186/s12880-025-01721-1Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications
Atmakuru, Anirudh, Chakraborty, Subrata, Salvi, Massimo, Faust, Oliver, Barua, Prabal Datta, Kobayashi, Makiko, Tan, Ru San, Molinari, Filippo, Hafeez-Baig, Abdul and Acharya, U. Rajendra. 2025. "Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications." IEEE Access. 13, pp. 25026-25044. https://doi.org/10.1109/ACCESS.2025.3539196Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
Tasci, Gulay, Barua, Prabal Datta, Tanko, Dahiru, Keles, Tugce, Tas, Suat, Tuncer, Ilknur, Kaya, Suheda, Yildirim, Kubra, Talu, Yunus, Tasci, Burak, Ozsoy, Filiz, Gonen, Nida, Tasci, Irem, Dogan, Sengul and Tuncer, Turker. 2025. "Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals." Diagnostics. 15 (2). https://doi.org/10.3390/diagnostics15020154FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection
Tuncer, Ilknur, Hafeez-Baig, Abdul, Barua, Prabal Datta, Hajiyeva, Rena, Massimo, Salvi, Dogan, Sengul, Tuncer, Turker and Acharya, U.R.. 2025. "FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection." Biomedical Signal Processing and Control. 104. https://doi.org/10.1016/j.bspc.2025.107538BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images
Poyraz, Melahat, Poyraz, Ahmet Kursad, Dogan, Yusuf, Gunes, Selva, Mir, Hasan S., Paul, Jose Kunnel, Barua, Prabal Datta, Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Molinari, Filippo and Acharya, Rajendra. 2025. "BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images." Cognitive Neurodynamics. 19 (1). https://doi.org/10.1007/s11571-025-10235-zMobileTransNeXt: Integrating CNN, transformer, and BiLSTM for image classification
Ye, Peishun, Lin, Jiyan, Kang, Yaming, Kaya, Tolga, Yildirim, Kubra, Hafeez Baig, Abdul, Aydemir, Emrah, Dogan, Sengul and Tuncer, Turker. 2025. "MobileTransNeXt: Integrating CNN, transformer, and BiLSTM for image classification." Alexandria Engineering Journal. 123, pp. 460-470. https://doi.org/10.1016/j.aej.2025.03.048CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals
Ince, Ugur, Talu, Yunus, Duz, Aleyna, Tas, Suat, Tanko, Dahiru, Tasci, Irem, Dogan, Sengul, Hafeez-Baig, Abdul, Aydemir, Emrah and Tuncer, Turker. 2025. "CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals." Diagnostics. 15 (3). https://doi.org/10.3390/diagnostics15030363Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images
Key, Sefa, Kurum, Huseyin, Esmez, Omer, Hafeez-Baig, Abdul, Hajiyeva, Rena, Dogan, Sengul and Tuncer, Turker. 2025. "Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images." Ain Shams Engineering Journal. 16 (1). https://doi.org/10.1016/j.asej.2024.103235Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)
Atmakuru, Anirudh, Shahini, Alen, Chakraborty, Subrata, Seoni, Silvia, Salvi, Massimo, Hafeez-Baig, Abdul, Rashid, Sadaf, Tan, Ru San, Barua, Prabal Datta, Molinari, Filippo and Acharya, U Rajendra. 2025. "Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)." Information Fusion. 114. https://doi.org/10.1016/j.inffus.2024.102673AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models
Aydin, Mehmet, Barua, Prabal Datta, Chadalavada, Sreenivasulu, Dogan, Sengul, Tuncer, Turker, Chakraborty, Subrata and Acharya, Rajendra U.. 2025. "AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models." Multimedia Tools and Applications. 84 (4), pp. 1821-1843. https://doi.org/10.1007/s11042-024-19163-2Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification
Tuncer, Turker, Dogan, Sengul, Baygin, Mehmet, Tasci, Irem, Mungen, Bulent, Tasci, Burak, Barua, Prabal Datta and Acharya, U.R.. 2024. "Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification." Knowledge-Based Systems. 305. https://doi.org/10.1016/j.knosys.2024.112555Automated EEG-based language detection using directed quantum pattern technique
Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta and Acharya, U.R.. 2024. "Automated EEG-based language detection using directed quantum pattern technique." Applied Soft Computing. 167 (Part A). https://doi.org/10.1016/j.asoc.2024.112301