Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping

Hussain, Tahir; Shouno, Hayaru

doi:10.3390/info14120642

Cited by 10 publications

(1 citation statement)

References 94 publications

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“…The use of neural networks remains unexplored for air quality zoning. It is true that the applications of neural networks for dimensionality reduction have been explored in other fields such as hydrology [29] or medical imaging processing [30]. Other unsupervised strategies for dimensionality reduction includes multiview fuzzy c-means clustering algorithms, a technique used in data analysis and machine learning to group data points into clusters when multiple perspectives or views of data are available [31].…”

Section: State Of the Artmentioning

confidence: 99%

Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

Fernández,

Valera,

Breis

2024

IEEE Access

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According to the Air Quality Directive 2008/50/EC, air quality zoning divides a territory into air quality zones where pollution and citizen exposure are similar and can be monitored using similar strategies. However, there is no standardized computational methodology to solve this problem, and only a few experiences in the Comunidad of Madrid based on CHIMERE-WRF. In this study, we propose a methodological improvement based on the application of deep learning. Our method uses the CHIMERE-WRF air quality modelling system and adds a step that uses neural networks architectures to calibrate the simulations. We have validated our method in the Region of Murcia. The results obtained are promising given the values of the Pearson coefficient, obtaining r = 0.94 for NO 2 and r = 0.95 for O 3 , improving 86 % and 29 % the performances reported in the state of the art. In addition, the cluster score improves after applying neural networks, demonstrating that neural networks improve the consistency of clusters compared to the current air quality zoning. This opened new research opportunities based on the use of neural networks for dimension reduction in spatial clustering problems, and we were able to provide recommendations for a new measurement point in the Region of Murcia Air Quality Network.

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Section: State Of the Artmentioning

confidence: 99%

Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

Fernández,

Valera,

Breis

2024

IEEE Access

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Cross-attention guided loss-based deep dual-branch fusion network for liver tumor classification

Wang,

Shi,

Pang

et al. 2025

Information Fusion

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MAGRes-UNet: Improved Medical Image Segmentation Through a Deep Learning Paradigm of Multi-Attention Gated Residual U-Net

Hussain,

Shouno

2024

IEEE Access

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Precise segmentation is vital for successful diagnosis and treatment planning. Medical image segmentation has demonstrated remarkable advances with the introduction of deep convolutional neural networks, particularly encoder-decoder networks such as U-Net. Despite their excellent performances, these methods have some limitations. First, the structure is limited in its ability to combine information because feature maps to extract valid information from the final encoding stage are incompatible at the encoding and decoding levels. Second, the approach ignores significant semantic details and does not consider different types of small-scale contextual information when segmenting medical images. Lastly, most methods employing 3D architectures to process input medical images increase the computational complexity of the model without significantly improving the accuracy. To resolve these issues, we propose a segmentation network called Multi-Attention Gated Residual U-Net (MAGRes-UNet). This network incorporates four multi-attention gate (MAG) modules and residual blocks into a standard U-Net structure. The MAG module integrates the information from all encoding stages and focuses on small-scale tumors while disambiguating irrelevant and noisy feature responses, thereby promoting meaningful contextual information. The residual blocks simplify the network training and mitigate the problem of vanishing gradients. This improves the ability of the network to effectively learn intricate features and deep representations. Moreover, our network employs the Mish and ReLU activation functions (AFs), which utilize AdamW and Adam optimization strategies to achieve enhanced segmentation performance. The proposed MAGRes-UNet method was compared with the U-Net, Multi-Attention Gated-UNet (MAG-UNet), and Residual-UNet (ResUNet) models. In addition, a statistical T-test was performed to assess the difference in model significance between the approaches. The analysis revealed that MAGRes-UNet employing Mish and AdamW provides significant performance improvement over the ReLU AF and Adam optimizer on two benchmark datasets: Multi-Class BT T1-weighted Contrast-Enhanced Magnetic Resonance Imaging (T1-CE-MRI) and skin lesions HAM10000 (Human Against Machine with 10,000 training images). MAGRes-UNet using Mish and AdamW provides competitive performance over the representative medical image segmentation methods.

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Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping

Cited by 10 publications

References 94 publications

Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

Cross-attention guided loss-based deep dual-branch fusion network for liver tumor classification

MAGRes-UNet: Improved Medical Image Segmentation Through a Deep Learning Paradigm of Multi-Attention Gated Residual U-Net

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