Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

Çifçi, Mehmet Akif; Hussain, Sadiq; Canatalay, Peren Jerfi

doi:10.3390/diagnostics13061025

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…The model proposed in this study, derived from the fine-tuned YOLOv5 architecture, underwent a comprehensive evaluation and comparison with contemporary methodologies for brain tumor detection, as outlined in [ 56 ]. In this section, we present a detailed analysis of its performance relative to these established techniques.…”

Section: Resultsmentioning

confidence: 99%

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Abdusalomov,

Rakhimov,

Karimberdiyev

et al. 2024

Bioengineering

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Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection’s robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modules are integrated within a unified framework. The brain tumor dataset is used to refine the YOLOv5 model through the application of transfer learning techniques, adapting it specifically to the task of tumor detection. The results indicate that the combination of YOLOv5 and other modules results in enhanced detection capabilities in comparison to the utilization of YOLOv5 exclusively, proving recall rates of 86% and 83% respectively. Moreover, the research explores the interpretability aspect of the combined model. By visualizing the attention maps generated by the NLNNs module, the regions of interest associated with tumor presence are highlighted, aiding in the understanding and validation of the decision-making procedure of the methodology. Additionally, the impact of hyperparameters, such as NLNNs kernel size, fusion strategy, and training data augmentation, is investigated to optimize the performance of the combined model.

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Section: Resultsmentioning

confidence: 99%

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Abdusalomov,

Rakhimov,

Karimberdiyev

et al. 2024

Bioengineering

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“…The use of CNN techniques in medical image analysis and disease classification necessitates the availability of comprehensive and diverse datasets [111][112][113]. The success of these techniques relies on the richness and representativeness of the datasets, as they enable the extraction of salient information and features from medical images and records [112,114,115].…”

Section: Image Datasets Relevant For Medical Themesmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation

Khaniabadi,

Ibrahim,

Huqqani

et al. 2023

2023 IEEE 14th Control and System Graduate Research Colloquium (ICSGRC)

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Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

Cited by 4 publications

References 20 publications

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation

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