Artificial Intelligence in Medicine: Enhancing Pneumonia Detection Using Wavelet Transform

Rakhimov, Mekhriddin; Karimberdiyev, Jakhongir; Javliev, Shakhzod

doi:10.1007/978-3-031-53827-8_16

Cited by 3 publications

(1 citation statement)

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“…Both patients and medical professionals dealing with the difficulties of treating brain malignancies stand to gain from the further development of brain tumor detection systems through ongoing research. We can increase diagnostic capabilities and, eventually, patient outcomes by improving detecting technologies and deepening our understanding of this field [ 74 , 75 ].…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%