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Journal articles on the topic 'Class Activation Maps(CAM)'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Debasis Chaudhuri, Akash Samanta, Aniket Kumar Singh, and Manish Pratap Singh. "Pixel Ablation-CAM: A New Paradigm in CNN Interpretability for Feature Map Visual Explanations." Defence Science Journal 75, no. 2 (2025): 188–98. https://doi.org/10.14429/dsj.20444.

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Many cutting-edge computer vision systems now rely heavily on convolutional neural networks, or CNNs. However, conventional interpretation techniques frequently concentrate on 2D feature maps, ignoring the intricate contributions of individual pixels. This work aims to produce “visual explanations” that improve the explainability and transparency of decisions made by various CNN-based algorithms. We provide Pixel Ablation-CAM, a new method that builds on the ideas of Ablation-CAM by using pixel-wise ablation, which enables a finer-grained comprehension of model choices. With this method, activ

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k, k., k. k, and k. k. "Integration of Multi-scale CAM and Attention for Weakly Supervised Defects Localization on Surface Defective Apple." Korean Institute of Smart Media 12, no. 9 (2023): 45–59. http://dx.doi.org/10.30693/smj.2023.12.9.45.

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Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated t

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Kusunose, Shoya, Yuki Shinomiya, Takashi Ushiwaka, Nagamasa Maeda, and Yukinobu Hoshino. "Enhancement of the Individual Selectness Using Local Spatial Weighting for Immune Cells." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (2022): 178–87. http://dx.doi.org/10.20965/jaciii.2022.p0178.

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This paper focuses on the analysis of the activity of immune cells for supporting medical workers. Recognition frequency space selects a region including neighboring multiple cells as a single cell is one of the major issues in activity analysis of immune cells. This study focuses on the locality of immune cell features and uses a high-velocity weighting method for the analysis while the Gaussian distribution is used in the literature. The analysis was conducted for a few well-known methods such as final feature maps, class activation mapping (CAM), gradient weighted class activation mapping (

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Kaczmarek, Emily, Olivier X. Miguel, Alexa C. Bowie, et al. "CAManim: Animating end-to-end network activation maps." PLOS ONE 19, no. 6 (2024): e0296985. http://dx.doi.org/10.1371/journal.pone.0296985.

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Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and

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Fu, Jia, Guotai Wang, Tao Lu, et al. "UM-CAM: Uncertainty-weighted multi-resolution class activation maps for weakly-supervised segmentation." Pattern Recognition 160 (April 2025): 111204. http://dx.doi.org/10.1016/j.patcog.2024.111204.

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Yang, Zhiwei, Yucong Meng, Kexue Fu, Shuo Wang, and Zhijian Song. "MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9400–9408. https://doi.org/10.1609/aaai.v39i9.33018.

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Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Localization Attention Maps (LAM) often struggle with the artifact issue, i.e., patch regions with minimal semantic relevance are falsely activated by class tokens. In this work, we propose MoRe to address this issue and further explore the poten

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Luo, Wenfeng, and Meng Yang. "Learning Saliency-Free Model with Generic Features for Weakly-Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11717–24. http://dx.doi.org/10.1609/aaai.v34i07.6842.

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Current weakly-supervised semantic segmentation methods often estimate initial supervision from class activation maps (CAM), which produce sparse discriminative object seeds and rely on image saliency to provide background cues when only class labels are used. To eliminate the demand of extra data for training saliency detector, we propose to discover class pattern inherent in the lower layer convolution features, which are scarcely explored as in previous CAM methods. Specifically, we first project the convolution features into a low-dimension space and then decide on a decision boundary to g

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Loaiza-Arias, Marcos, Andrés Marino Álvarez-Meza, David Cárdenas-Peña, Álvaro Ángel Orozco-Gutierrez, and German Castellanos-Dominguez. "Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification." Applied Sciences 14, no. 23 (2024): 11208. https://doi.org/10.3390/app142311208.

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Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. Among these, motor imagery (MI), in which patients mentally simulate motor tasks without physical movement, has proven to be an effective paradigm for diagnosing and monitoring neurological conditions. Electroencephalography (EEG) is widely used for MI data collection due to its high temporal resolution, cost-effectiveness, and portability. However, EEG signals can be noisy from a number of sources, including physiological artifacts and elec

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Fahim, Masud An Nur Islam, Nazmus Saqib, Shafkat Khan Siam, and Ho Yub Jung. "Rethinking Gradient Weight’s Influence over Saliency Map Estimation." Sensors 22, no. 17 (2022): 6516. http://dx.doi.org/10.3390/s22176516.

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Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing model’s layer response and the influences of the gradients. Typical gradient-oriented CAM studies rely on weighted aggregation for saliency map estimation by projecting the gradient maps into single-weight values, which may lead to an over-generalized saliency map. To address this issue, w

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Si, Nianwen, Wenlin Zhang, Dan Qu, Xiangyang Luo, Heyu Chang, and Tong Niu. "Spatial-Channel Attention-Based Class Activation Mapping for Interpreting CNN-Based Image Classification Models." Security and Communication Networks 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/6682293.

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Convolutional neural network (CNN) has been applied widely in various fields. However, it is always hindered by the unexplainable characteristics. Users cannot know why a CNN-based model produces certain recognition results, which is a vulnerability of CNN from the security perspective. To alleviate this problem, in this study, the three existing feature visualization methods of CNN are analyzed in detail firstly, and a unified visualization framework for interpreting the recognition results of CNN is presented. Here, class activation weight (CAW) is considered as the most important factor in

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Feng, Zhenpeng, Hongbing Ji, Ljubiša Stanković, Jingyuan Fan, and Mingzhe Zhu. "SC-SM CAM: An Efficient Visual Interpretation of CNN for SAR Images Target Recognition." Remote Sensing 13, no. 20 (2021): 4139. http://dx.doi.org/10.3390/rs13204139.

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Convolutional neural networks (CNNs) have successfully achieved high accuracy in synthetic aperture radar (SAR) target recognition; however, the intransparency of CNNs is still a limiting or even disqualifying factor. Therefore, visually interpreting CNNs with SAR images has recently drawn increasing attention. Various class activation mapping (CAM) methods are adopted to discern the relationship between CNN’s decision and image regions. Unfortunately, most existing CAM methods are based on optical images; thus, they usually lead to a limiting visualization effect for SAR images. Although a re

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Rahman, Md Fashiar, Tzu-Liang (Bill) Tseng, Michael Pokojovy, et al. "Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images." Diagnostics 14, no. 16 (2024): 1699. http://dx.doi.org/10.3390/diagnostics14161699.

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The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first p

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Bouaouina, Rafik, Amir Benzaoui, Hakim Doghmane, and Youcef Brik. "Boosting the Performance of Deep Ear Recognition Systems Using Generative Adversarial Networks and Mean Class Activation Maps." Applied Sciences 14, no. 10 (2024): 4162. http://dx.doi.org/10.3390/app14104162.

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Ear recognition is a complex research domain within biometrics, aiming to identify individuals using their ears in uncontrolled conditions. Despite the exceptional performance of convolutional neural networks (CNNs) in various applications, the efficacy of deep ear recognition systems is nascent. This paper proposes a two-step ear recognition approach. The initial step employs deep convolutional generative adversarial networks (DCGANs) to enhance ear images. This involves the colorization of grayscale images and the enhancement of dark shades, addressing visual imperfections. Subsequently, a f

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Ornek, Ahmet H., and Murat Ceylan. "HayCAM: A Novel Visual Explanation for Deep Convolutional Neural Networks." Traitement du Signal 39, no. 5 (2022): 1711–19. http://dx.doi.org/10.18280/ts.390529.

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Explaining the decision mechanism of Deep Convolutional Neural Networks (CNNs) is a new and challenging area because of the “Black Box” nature of CNN's. Class Activation Mapping (CAM) as a visual explainable method is used to highlight important regions of input images by using classification gradients. The lack of the current methods is to use all of the filters in the last convolutional layer which causes scattered and unfocused activation mapping. HayCAM as a novel visualization method provides better activation mapping and therefore better localization by using dimension reduction. It has

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Pe, Samuele, Lorenzo Famiglini, Enrico Gallazzi, et al. "Alternative Strategies to Generate Class Activation Maps Supporting AI-based Advice in Vertebral Fracture Detection in X-ray Images." Methods of Information in Medicine 63, no. 03/04 (2024): 122–36. https://doi.org/10.1055/a-2562-2163.

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AbstractBalancing artificial intelligence (AI) support with appropriate human oversight is challenging, with associated risks such as algorithm aversion and technology dominance. Research areas like eXplainable AI (XAI) and Frictional AI aim to address these challenges. Studies have shown that presenting XAI explanations as “juxtaposed evidence” supporting contrasting classifications, rather than just providing predictions, can be beneficial.This study aimed to design and compare multiple pipelines for generating juxtaposed evidence in the form of class activation maps (CAMs) that highlight ar

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McAllister, Dianna, Mauro Mendez, Ariana Bermúdez, and Pascal Tyrrell. "Visualization of Layers Within a Convolutional Neural Network Using Gradient Activation Maps." Journal of Undergraduate Life Sciences 14, no. 1 (2020): 6. http://dx.doi.org/10.33137/juls.v14i1.35833.

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Introduction: Convolutional neural networks (CNNs) are machine learning tools that have great potential in the field of medical imaging. However, it is often regarded as a “black box” as the process that is used by the machine to acquire a result is not transparent. It would be valuable to find a method to be able to understand how the machine comes to its decision. Therefore, the purpose of this study is to examine how effective gradient-weighted class activation mapping (grad-CAM) visualizations are for certain layers in a CNN-based dental x-ray artifact prediction model. Methods: To t

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Liu, Haipeng, Yibo Zhao, Meng Wang, Meiyan Ma, and Zhaoyu Chen. "Activation extending based on long-range dependencies for weakly supervised semantic segmentation." PLOS ONE 18, no. 11 (2023): e0288596. http://dx.doi.org/10.1371/journal.pone.0288596.

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Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided int

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Chien, Jong-Chih, Jiann-Der Lee, Ching-Shu Hu, and Chieh-Tsai Wu. "The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses." Applied Sciences 12, no. 15 (2022): 7748. http://dx.doi.org/10.3390/app12157748.

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In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion or tumor not visible to the human eye, and may also help provide additional recessive information by using modern data analysis methods. These methods, including Artificial Intelligence (AI) technologies, are based on deep learning architectures, and have shown remarkable results in recent studies. However, the lack of explanatory ability of connec

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Gao, Chenxiang, and Wei Wu. "Boosting the Transferability of Adversarial Examples with More Efficient Data Augmentation." Journal of Physics: Conference Series 2189, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2189/1/012025.

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Abstract Currently, Deep Neural Networks has achieved excellent results on many tasks. But recent studies have shown that these networks are easily influenced by adversarial examples, which are artificially crafted by adding perturbation to original image. Moreover, most of the models to which we can access are black-box, we don’t know the internal structure and parameters of the model. Thus, it is more practical and more challenging to study how to attack these models. In this article, we propose a cam(class activation map)-guided data augmentation attack method, which can improve the transfe

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Shinde, Sumeet, Priyanka Tupe-Waghmare, Tanay Chougule, Jitender Saini, and Madhura Ingalhalikar. "Predictive and discriminative localization of pathology using high resolution class activation maps with CNNs." PeerJ Computer Science 7 (July 14, 2021): e622. http://dx.doi.org/10.7717/peerj-cs.622.

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Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a single layer of the convolutional neural net (CNN), generally from the final layer and then interpolate to upsample to the original image resolution to locate the discriminative regions. Consequently these provide a coarse localization that may not be able to capture subtle abnormalities in medical images. To alleviate this, our work proposes a technique called high resolution class activation mapping (HR-CAMs) that can provide enhanced visual explainability to the CNN models. Methods HR-CAMs fuse f

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Chavarro, Adrian, Diego Renza, and Ernesto Moya-Albor. "ConvNext as a Basis for Interpretability in Coffee Leaf Rust Classification." Mathematics 12, no. 17 (2024): 2668. http://dx.doi.org/10.3390/math12172668.

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The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and eXplainable Artificial Intelligence (XAI) come into play to facilitate an understanding of the inner workings of models. Consequently, alternatives have emerged, such as class activation mapping (CAM) techniques aimed at identifying regions of importance for an image classification model. However, the behavior of such models can be highly dependent on the type of architecture and the different variants of convolutional

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Gao, Yongshun, Jie Liu, Weihan Li, Ming Hou, Yang Li, and Huimin Zhao. "Augmented Grad-CAM++: Super-Resolution Saliency Maps for Visual Interpretation of Deep Neural Network." Electronics 12, no. 23 (2023): 4846. http://dx.doi.org/10.3390/electronics12234846.

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In recent years, deep neural networks have shown superior performance in various fields, but interpretability has always been the Achilles’ heel of deep neural networks. The existing visual interpretation methods for deep neural networks still suffer from inaccurate and insufficient target localization and low-resolution saliency maps. To address the above issues, this paper presents a saliency map generation method based on image geometry augmentation and super-resolution called augmented high-order gradient weighting class activation mapping (augmented grad-CAM++). Unlike previous approaches

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Carofilis, Andrés, Enrique Alegre, Eduardo Fidalgo, and Laura Fernández-Robles. "Improvement of Accent Classification Models Through Grad-Transfer From Spectrograms and Gradient-Weighted Class Activation Mapping." IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (July 21, 2023): 2859–71. https://doi.org/10.1109/TASLP.2023.3297961.

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Automatic accent classification is an active research field concerning speech processing. It can be useful to identify a speaker's region of origin, which can be applied in police investigations carried out by Law Enforcement Agencies, as well as for the improvement of current speech recognition systems. This article presents a novel descriptor called Grad-Transfer, extracted using the Gradient-weighted Class Activation Mapping (Grad-CAM) method based on convolutional neural network (CNN) interpretability. Additionally, we propose a methodology for accent classification that implements Grad-Tr

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Chen, Yunxia, Yangkai He, and Yukun Chu. "Impact of Activation Functions on the Detection of Defects in Cast Steel Parts Using YOLOv8." Materials 18, no. 12 (2025): 2834. https://doi.org/10.3390/ma18122834.

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In this paper, to address the issue of the unknown influence of activation functions on casting defect detection using convolutional neural networks (CNNs), we designed five sets of experiments to investigate how different activation functions affect the performance of casting defect detection. Specifically, the study employs five activation functions—Rectified Linear Unit (ReLU), Exponential Linear Units (ELU), Softplus, Sigmoid Linear Unit (SiLU), and Mish—each with distinct characteristics, based on the YOLOv8 algorithm. The results indicate that the Mish activation function yields the best

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Qiu, Changqing, Fusheng Jin, and Yining Zhang. "Empowering CAM-Based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4587–95. http://dx.doi.org/10.1609/aaai.v38i5.28258.

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Recently, the explanation of neural network models has garnered considerable research attention. In computer vision, CAM (Class Activation Map)-based methods and LRP (Layer-wise Relevance Propagation) method are two common explanation methods. However, since most CAM-based methods can only generate global weights, they can only generate coarse-grained explanations at a deep layer. LRP and its variants, on the other hand, can generate fine-grained explanations. But the faithfulness of the explanations is too low. To address these challenges, in this paper, we propose FG-CAM (Fine-Grained CAM),

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Kasireddy, Harishwar Reddy, Udaykanth Reddy Kallam, Sowmitri Karthikeya Siddhartha Mantrala, et al. "Deep-Learning-Based Visualization and Volumetric Analysis of Fluid Regions in Optical Coherence Tomography Scans." Diagnostics 13, no. 16 (2023): 2659. http://dx.doi.org/10.3390/diagnostics13162659.

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Retinal volume computation is one of the critical steps in grading pathologies and evaluating the response to a treatment. We propose a deep-learning-based visualization tool to calculate the fluid volume in retinal optical coherence tomography (OCT) images. The pathologies under consideration are Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigmented Epithelial Detachment (PED). We develop a binary classification model for each of these pathologies using the Inception-ResNet-v2 and the small Inception-ResNet-v2 models. For visualization, we use several standard Class Activation Mappi

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Altini, Nicola, Antonio Brunetti, Emilia Puro, et al. "NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM." Bioengineering 9, no. 9 (2022): 475. http://dx.doi.org/10.3390/bioengineering9090475.

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Nuclei identification is a fundamental task in many areas of biomedical image analysis related to computational pathology applications. Nowadays, deep learning is the primary approach by which to segment the nuclei, but accuracy is closely linked to the amount of histological ground truth data for training. In addition, it is known that most of the hematoxylin and eosin (H&E)-stained microscopy nuclei images contain complex and irregular visual characteristics. Moreover, conventional semantic segmentation architectures grounded on convolutional neural networks (CNNs) are unable to recogniz

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Feng, Jiahao, Ce Li, and Jin Wang. "CAM-TMIL: A Weakly-Supervised Segmentation Framework for Histopathology based on CAMs and MIL." Journal of Physics: Conference Series 2547, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2547/1/012014.

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Abstract Semantic segmentation plays a significant role in histopathology by assisting pathologists in diagnosis. Although fully-supervised learning achieves excellent success on segmentation for histopathological images, it costs pathologists and experts great efforts on pixel-level annotation in the meantime. Thus, to reduce the annotation workload, we proposed a weakly-supervised learning framework called CAM-TMIL, which assembles methods based on class activation maps (CAMs) and multiple instance learning (MIL) to perform segmentation with image-level labels. By leveraging the MIL method,

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Saito, Michiyuki, Mizuho Mitamura, Mayuko Kimura, et al. "Grad-CAM-Based Investigation into Acute-Stage Fluorescein Angiography Images to Predict Long-Term Visual Prognosis of Branch Retinal Vein Occlusion." Journal of Clinical Medicine 13, no. 17 (2024): 5271. http://dx.doi.org/10.3390/jcm13175271.

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Background/Objectives: The purpose of this study was to analyze relevant areas in acute-stage fluorescein angiography (FA) images, predicting the long-term visual prognosis of branch retinal vein occlusion (BRVO) based on gradient-weighted class activation mapping (Grad-CAM). Methods: This retrospective observational study included 136 eyes with BRVO that were followed up for more than a year post-FA. Cropped grayscale images centered on the fovea (200 × 200 pixels) were manually pre-processed from early-phase FA at the acute phase. Pairs of the cropped FA images and the best-corrected visual

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Feng, Zhenpeng, Mingzhe Zhu, Ljubiša Stanković, and Hongbing Ji. "Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation." Remote Sensing 13, no. 9 (2021): 1772. http://dx.doi.org/10.3390/rs13091772.

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Synthetic aperture radar (SAR) image interpretation has long been an important but challenging task in SAR imaging processing. Generally, SAR image interpretation comprises complex procedures including filtering, feature extraction, image segmentation, and target recognition, which greatly reduce the efficiency of data processing. In an era of deep learning, numerous automatic target recognition methods have been proposed based on convolutional neural networks (CNNs) due to their strong capabilities for data abstraction and mining. In contrast to general methods, CNNs own an end-to-end structu

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Pinciroli Vago, Nicolò Oreste Pinciroli, Federico Milani, Piero Fraternali, and Ricardo da Silva Torres. "Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis." Journal of Imaging 7, no. 7 (2021): 106. http://dx.doi.org/10.3390/jimaging7070106.

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Iconography studies the visual content of artworks by considering the themes portrayed in them and their representation. Computer Vision has been used to identify iconographic subjects in paintings and Convolutional Neural Networks enabled the effective classification of characters in Christian art paintings. However, it still has to be demonstrated if the classification results obtained by CNNs rely on the same iconographic properties that human experts exploit when studying iconography and if the architecture of a classifier trained on whole artwork images can be exploited to support the muc

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Huang, Guanglun, Zhaohao Zheng, Jun Li, Minghe Zhang, Jianming Liu, and Li Zhang. "Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation." Applied Sciences 15, no. 12 (2025): 6474. https://doi.org/10.3390/app15126474.

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Image-level weakly supervised semantic segmentation is a challenging problem in computer vision and has gained a lot of attention in recent years. Most existing models utilize class activation mapping (CAM) to generate initial pseudo-labels for each image pixel. However, CAM usually focuses only on the most discriminating regions of target objects and treats each channel feature map independently, which may overlook some important regions due to the lack of accurate pixel-level labels, leading to the underactivation of the target objects. In this paper, we propose a dual attention equivariant

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Song, Boyue, and Shinichi Yoshida. "Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer’s disease classification based on gradient-weighted class activation mapping." PLOS ONE 19, no. 5 (2024): e0303278. http://dx.doi.org/10.1371/journal.pone.0303278.

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Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer’s Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying several prevalent explainability methods such as gradient-weighted class activation mapping (Grad-CAM) to an fMRI-based 3D-VGG16 network for AD diagnosis to improve the model’s explainability. The aim is to explore the specific Region of Interest (ROI) of brain the model primarily focuses on when making predictions, as well as whether there are differ

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Menon, P. Archana, and Dr R. Gunasundari. "Deep Feature Extraction and Classification of Alzheimer's Disease: A Novel Fusion of Vision Transformer-DenseNet Approach with Visualization." Journal of Internet Services and Information Security 14, no. 4 (2024): 462–83. https://doi.org/10.58346/jisis.2024.i4.029.

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Alzheimer's Disease (AD) classification from brain MRI images remains a strenuous mission due to the intricacy of the disease and limited dataset sizes. Eventhough Convolutional Neural Networks (CNNs) have excelled in the classification of brain diseases using MRI data, they are incompetent to apprehend global dependencies. Also, their results are not interpretable which is a major problem in medical domain. Transformer uses attention mechanisms to go with or even surpass CNNs on various vision tasks. This study proposes a novel fusion model integrating the complementary advantages of DenseNet

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Im, Chanjong, Yongho Kim, and Thomas Mandl. "Deep learning for historical books: classification of printing technology for digitized images." Multimedia Tools and Applications 81, no. 4 (2021): 5867–88. http://dx.doi.org/10.1007/s11042-021-11754-7.

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AbstractPrinting technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized

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Li, Jun, Daoyu Lin, Yang Wang, et al. "Deep Discriminative Representation Learning with Attention Map for Scene Classification." Remote Sensing 12, no. 9 (2020): 1366. http://dx.doi.org/10.3390/rs12091366.

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In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representations extracted from CNNs is still limited in distinguishing more complex remote sensing images. Therefore, we propose a unified feature fusion framework based on attention mechanism in this paper, which is called Deep Discriminative Representation Learning with Attention Map (DDRL-AM). Firstly, by applying Gradient-weighted Class Activation Mapping

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Renza, Diego, and Dora Ballesteros. "Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images." Informatics 10, no. 3 (2023): 72. http://dx.doi.org/10.3390/informatics10030072.

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CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, which is known as pruning. Typically, pruning methods are compared in terms of performance (e.g., accuracy), model size and inference speed. However, it is unusual to evaluate whether a pruned model preserves regions of importance in an image when performing inference. Consequently, we propose a metri

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Ishihara, Yuta, Ken’ichi Fujimoto, Hiroshi Murai, Junko Ishikawa, and Dai Mitsushima. "Classification of Hippocampal Ripples: Convolutional Neural Network Learns Episode-Specific Changes." Brain Sciences 14, no. 2 (2024): 177. http://dx.doi.org/10.3390/brainsci14020177.

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The hippocampus is known to play an important role in memory by processing spatiotemporal information of episodic experiences. By recording synchronized multiple-unit firing events (ripple firings with 300 Hz–10 kHz) of hippocampal CA1 neurons in freely moving rats, we previously found an episode-dependent diversity in the waveform of ripple firings. In the present study, we hypothesized that changes in the diversity would depend on the type of episode experienced. If this hypothesis holds, we can identify the ripple waveforms associated with each episode. Thus, we first attempted to classify

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Walker, Mark C., Inbal Willner, Olivier X. Miguel, et al. "Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester." PLOS ONE 17, no. 6 (2022): e0269323. http://dx.doi.org/10.1371/journal.pone.0269323.

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Objective To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. Methods All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to

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Zhu, Xiaolong, Junhong Zhang, Xinwei Wang, Hui Wang, and Jiewei Lin. "A multi-modal joint attention network for vibro-acoustic fusion diagnosis of engines." Measurement Science and Technology 35, no. 9 (2024): 096104. http://dx.doi.org/10.1088/1361-6501/ad4fb4.

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Abstract Deep learning has proven to be effective in diagnosing faults in power machinery and its diagnosis performance relies on a sufficient data set. In practice, a well-labeled data set with sufficient samples is very rare, especially for those machinery running in varying loading cases. The situation is particularly pronounced for multi-cylinder internal combustion engines, where the excitations from cylinders interact with significant background noise, and different data distributions are complicated. To tackle these issues, we propose a novelty multi-modal joint attention network (MJA-N

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Aguirre-Arango, Juan Carlos, Andrés Marino Álvarez-Meza, and German Castellanos-Dominguez. "Feet Segmentation for Regional Analgesia Monitoring Using Convolutional RFF and Layer-Wise Weighted CAM Interpretability." Computation 11, no. 6 (2023): 113. http://dx.doi.org/10.3390/computation11060113.

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Regional neuraxial analgesia for pain relief during labor is a universally accepted, safe, and effective procedure involving administering medication into the epidural. Still, an adequate assessment requires continuous patient monitoring after catheter placement. This research introduces a cutting-edge semantic thermal image segmentation method emphasizing superior interpretability for regional neuraxial analgesia monitoring. Namely, we propose a novel Convolutional Random Fourier Features-based approach, termed CRFFg, and custom-designed layer-wise weighted class-activation maps created expli

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Sharada, Gupta, and N. Eshwarappa Murundi. "Breast cancer detection through attention based feature integration model." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2254–64. https://doi.org/10.11591/ijai.v13.i2.pp2254-2264.

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Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM)

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Cho, Soo-Hyun, Seung-Woo Kang, Baek-Gyeom Sung, and Dae-Hyun Lee. "CAM-based orchard path detection for developing an unmanned sprayer." Precision Agriculture Science and Technology 5, no. 3 (2023): 163–68. http://dx.doi.org/10.12972/pastj.20230013.

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This study was conducted to apply a deep learning model to identify and visualise path areas in an orchard. Data was collected by attaching an image capture device to the front of a sprayer and driving it through an orchard. The collected data was classified into four classes: ground, trees, sky and obstacles for pre-processing for training. Sliding window techniques were used on the image dataset for model training and performance. The image was sampled using a sliding window method with 224x224 pixels and divided into train, validation, and test sets. A modified VGG16 algorithm was implement

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Zhang, Hongjian, and Katsuhiko Ogasawara. "Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing." Bioengineering 10, no. 9 (2023): 1070. http://dx.doi.org/10.3390/bioengineering10091070.

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The opacity of deep learning makes its application challenging in the medical field. Therefore, there is a need to enable explainable artificial intelligence (XAI) in the medical field to ensure that models and their results can be explained in a manner that humans can understand. This study uses a high-accuracy computer vision algorithm model to transfer learning to medical text tasks and uses the explanatory visualization method known as gradient-weighted class activation mapping (Grad-CAM) to generate heat maps to ensure that the basis for decision-making can be provided intuitively or via

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Moud, Deepak. "Recognition of COVID-19 Affected Lungs Using CNN and Grad-Cam on Chest X-Ray." ECS Transactions 107, no. 1 (2022): 7251–63. http://dx.doi.org/10.1149/10701.7251ecst.

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The COVID-19 disease which was initially originated from Wuhan, China continues hampering people's lives. With mutation (change in DNA) the virus is transforming into several different strains and causing respiratory problems by affecting alveoli in the human lungs. The latest strain B.1.1.529 has been named as an “Omicron” variant by the WHO. Due to the large numbers of cases reported every day there is a shortage of test kits in hospitals. Considering this factor we will be building a deep learning convolutional neural network that will classify an X-ray image between normal and infected lun

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Tareke, Tewele W., Sarah Leclerc, Catherine Vuillemin, et al. "Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks." Journal of Imaging 10, no. 8 (2024): 203. http://dx.doi.org/10.3390/jimaging10080203.

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Objective: In clinical practice, thyroid nodules are typically visually evaluated by expert physicians using 2D ultrasound images. Based on their assessment, a fine needle aspiration (FNA) may be recommended. However, visually classifying thyroid nodules from ultrasound images may lead to unnecessary fine needle aspirations for patients. The aim of this study is to develop an automatic thyroid ultrasound image classification system to prevent unnecessary FNAs. Methods: An automatic computer-aided artificial intelligence system is proposed for classifying thyroid nodules using a fine-tuned deep

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Guptha, Sharada, and Murundi N. Eshwarappa. "Breast cancer detection through attention based feature integration model." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2254. http://dx.doi.org/10.11591/ijai.v13.i2.pp2254-2264.

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<span lang="EN-US">Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-latera

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Joshi, Snehal. "Deep Learning-Driven Pneumonia Classification: Leveraging CNNModelsfor Multi-Cause Diagnosis from Chest X-rays." International Journal of Engineering and Techniques(IJET) 11, no. 1 (2025): 96–105. https://doi.org/10.5281/zenodo.15303211.

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Research Paper Title" Deep Learning-Driven Pneumonia Classification: Leveraging CNNModelsfor Multi-Cause Diagnosis from Chest X-rays Journal: International Journal of Engineering and Techniques(IJET) Volume 11 Issue 1, February2025 This research presented a deep learning-based approach for diagnosing and classifying pneumonia into bacterial, viral, and fungal types using chest X-ray images. Leveraging publicly available datasets, we implemented and evaluated two powerful CNN architectures—ResNet-50 and DenseNet-121—to perform multi-class clas

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Gupta, Ayush, Jeya Mala D., Vishal Kumar Yadav, and Mayank Arora. "Dissecting Retinal Disease: A Multi-Modal Deep Learning Approach with Explainable AI for Disease Classification across Various Classes." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 02 (2025): 38–51. https://doi.org/10.3991/ijoe.v21i02.51409.

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This study investigates the efficacy of various deep learning (DL) models in detecting retinal diseases, specifically focusing on cataract detection. Utilizing a pre-processed fundus images data set classified into normal and cataract classes, we evaluate the performance of ResNet, VGG-16 and VGG-19 models based on accuracy, sensitivity, and specificity in classifying fundus images. The primary objective of this work is to provide explanations on the predictions done by the said DL models in order to ensure the ground-truth verification. The explanation is achieved using the explainable artifi

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Patro, Badri, Anupriy, and Vinay Namboodiri. "Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11848–55. http://dx.doi.org/10.1609/aaai.v34i07.6858.

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In this paper, we aim to obtain improved attention for a visual question answering (VQA) task. It is challenging to provide supervision for attention. An observation we make is that visual explanations as obtained through class activation mappings (specifically Grad-CAM) that are meant to explain the performance of various networks could form a means of supervision. However, as the distributions of attention maps and that of Grad-CAMs differ, it would not be suitable to directly use these as a form of supervision. Rather, we propose the use of a discriminator that aims to distinguish samples o

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