State of the Art and Prediction Model for Brain Tumor Detection

Pareek, Kamini; Tiwari, Pradeep Kumar; Bhatnagar, Vaibhav

doi:10.1007/978-981-16-2877-1_51

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…MRI sequences from 165 child subjects were evaluated, and the DICE similarity coefficient was 0.779. Pareek et al [27] used Sklearn's multilayer perceptron algorithm in the CNN model to learn both nonlinear and linear models and evaluated the CNN model by using the confusion matrix, achieving 86.63% accuracy in Kaggle's dataset. Saba et al [22] applied VGG-19 in the series with handmarked features, and the DSC test results on BRATS 2015, BRATS 2016, and BRATS 2017 were 0.99, 1.00, and 0.99, respectively.…”

Section: Image Processing Methods According To the Image Characterist...mentioning

confidence: 99%

Advances in Computer-Aided Medical Image Processing

Chen

Chi

2023

Applied Sciences

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The primary objective of this study is to provide an extensive review of deep learning techniques for medical image recognition, highlighting their potential for improving diagnostic accuracy and efficiency. We systematically organize the paper by first discussing the characteristics and challenges of medical imaging techniques, with a particular focus on magnetic resonance imaging (MRI) and computed tomography (CT). Subsequently, we delve into direct image processing methods, such as image enhancement and multimodal medical image fusion, followed by an examination of intelligent image recognition approaches tailored to specific anatomical structures. These approaches employ various deep learning models and techniques, including convolutional neural networks (CNNs), transfer learning, attention mechanisms, and cascading strategies, to overcome challenges related to unclear edges, overlapping regions, and structural distortions. Furthermore, we emphasize the significance of neural network design in medical imaging, concentrating on the extraction of multilevel features using U-shaped structures, dense connections, 3D convolution, and multimodal feature fusion. Finally, we identify and address the key challenges in medical image recognition, such as data quality, model interpretability, generalizability, and computational resource requirements. By proposing future directions in data accessibility, active learning, explainable AI, model robustness, and computational efficiency, this study paves the way for the successful integration of AI in clinical practice and enhanced patient care.

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Section: Image Processing Methods According To the Image Characterist...mentioning

confidence: 99%

Advances in Computer-Aided Medical Image Processing

Chen

Chi

2023

Applied Sciences

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Advancing Medical Diagnostics on Computer-Assisted Analysis for Digital Medicinal Imagery

Kumar,

Tyagi,

Singh

et al. 2024

Advances in Medical Technologies and Clinical Practice

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Digital medicinal imagery, comprising MRI, CT scans, and PET scans, constitutes a cornerstone of contemporary medical diagnostics. However, interpreting these intricate images presents formidable challenges, demanding considerable expertise and time. Computer-assisted analysis emerges as a promising approach to augment the accuracy and efficiency of medical diagnosis. This research proposal delineates a comprehensive study aimed at pioneering advanced computer-assisted analysis techniques tailored for digital medicinal imagery. The proposed study on investigating cutting-edge machine learning algorithms suitable for analyzing digital medicinal imagery, devising novel algorithms for automated disease detection, diagnosis, and treatment planning based on medical imaging data, rigorously evaluating the performance of these algorithms against existing methods through robust validation studies, and assessing the clinical feasibility and utility of integrating computer-assisted analysis tools into routine clinical practice.

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State of the Art and Prediction Model for Brain Tumor Detection

Cited by 2 publications

References 15 publications

Advances in Computer-Aided Medical Image Processing

Advances in Computer-Aided Medical Image Processing

Advancing Medical Diagnostics on Computer-Assisted Analysis for Digital Medicinal Imagery

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