Evaluating CNN Architectures Using Attention Mechanisms: Convolutional Block Attention Module, Squeeze, and Excitation for Image Classification on CIFAR10 Dataset

Ganguly, Abhisek; Ruby, A. Usha; J, George Chellin Chandran

doi:10.21203/rs.3.rs-3245213/v1

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…The MaxPool and AvgPool operations denote the max pooling and average pooling operations over the spatial dimensions of the feature maps, respectively. Equation (2) ( [36]) demonstrates the calculation for the spatial attention block weights:…”

Section: Pre-trained Vision Transformer (Vit)mentioning

confidence: 99%

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Abou Ali,

Dornaika,

Arganda-Carreras

2023

Algorithms

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Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

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Section: Pre-trained Vision Transformer (Vit)mentioning

confidence: 99%

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Abou Ali,

Dornaika,

Arganda-Carreras

2023

Algorithms

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Jordanian banknote data recognition: A CNN-based approach with attention mechanism

Nasayreh,

Jaradat,

Gharaibeh

et al. 2024

Journal of King Saud University - Computer and Information Scie

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Camera-Radar Fusion with Radar Channel Extension and Dual-CBAM-FPN for Object Detection

Sun,

Jiang,

Qin

et al. 2024

Sensors

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When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS.

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Evaluating CNN Architectures Using Attention Mechanisms: Convolutional Block Attention Module, Squeeze, and Excitation for Image Classification on CIFAR10 Dataset

Cited by 3 publications

References 30 publications

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Jordanian banknote data recognition: A CNN-based approach with attention mechanism

Camera-Radar Fusion with Radar Channel Extension and Dual-CBAM-FPN for Object Detection

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