Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization

Abhishek, Arjun; Jha, Rajib Kumar; Sinha, Rupam; Jha, Kamlesh

doi:10.1016/j.bspc.2023.104722

Cited by 36 publications

(6 citation statements)

References 47 publications

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“…Compared with the images before and after enhancement, the raw cell images appear blurry with insufficient details, whereas the contrast of the enhanced cell images is significantly improved, with more prominent light and dark areas and sharper edge details. To gain a more intuitive understanding of the impact of channel attention mechanisms in network models, we utilized Grad CAM [43] to obtain thermal maps based on the weight of test dataset samples. As shown in Figure 3b, the color gradient ranging from blue to red represents gradually increasing weights, with higher weights indicating more salient cell features that the model should pay greater attention to.…”

Section: Resultsmentioning

confidence: 99%

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells

Zheng,

Huang,

Chen

et al. 2023

Sensors

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The differential count of white blood cells (WBCs) can effectively provide disease information for patients. Existing stained microscopic WBC classification usually requires complex sample-preparation steps, and is easily affected by external conditions such as illumination. In contrast, the inconspicuous nuclei of stain-free WBCs also bring great challenges to WBC classification. As such, image enhancement, as one of the preprocessing methods of image classification, is essential in improving the image qualities of stain-free WBCs. However, traditional or existing convolutional neural network (CNN)-based image enhancement techniques are typically designed as standalone modules aimed at improving the perceptual quality of humans, without considering their impact on advanced computer vision tasks of classification. Therefore, this work proposes a novel model, UR-Net, which consists of an image enhancement network framed by ResUNet with an attention mechanism and a ResNet classification network. The enhancement model is integrated into the classification model for joint training to improve the classification performance for stain-free WBCs. The experimental results demonstrate that compared to the models without image enhancement and previous enhancement and classification models, our proposed model achieved a best classification performance of 83.34% on our stain-free WBC dataset.

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Section: Resultsmentioning

confidence: 99%

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells

Zheng,

Huang,

Chen

et al. 2023

Sensors

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“…These heatmaps are then superimposed on the images, which help the users to see the areas of the image that are most valuable for the model predictions. GradCAM follows three steps [15], [18], [54] to generate the heat maps, as shown below: Step 2: Global average pool the gradients to obtain the weights 𝛼 𝑘 𝑐 as given by Eq. ( 3).…”

Section: ) Gradcam Explanationsmentioning

confidence: 99%

An Explainable Artificial Intelligence Integrated System for Automatic Detection of Dengue From Images of Blood Smears Using Transfer Learning

Mayrose,

Sampathila,

Bairy

et al. 2024

IEEE Access

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Dengue fever is a rapidly increasing mosquito-borne ailment spread by the virus DENV in the tropics and subtropics worldwide. It is a significant public health problem and accounts for many deaths globally. Implementing more effective methods that can more accurately detect dengue cases is challenging. The theme of this digital pathology-associated research is automatic dengue detection from peripheral blood smears (PBS) employing deep learning (DL) techniques. In recent years, DL has been significantly employed for automated computer-assisted diagnosis of various diseases from medical images. This paper explores pre-trained convolution neural networks (CNNs) for automatic dengue fever detection. Transfer learning (TL) is executed on three state-of-the-art CNNs -ResNet50, MobileNetV3Small, and MobileNetV3Large, to customize the models for differentiating the dengueinfected blood smears from the healthy ones. The dataset used to design and test the models contains 100x magnified dengue-infected and healthy control digital microscopic PBS images. The models are validated with a 5-fold crossvalidation framework and tested on unseen data. An explainable artificial intelligence (XAI) approach, Gradientweighted Class Activation Mapping (GradCAM), is eventually applied to the models to allow visualization of the precise regions on the smears most instrumental in making the predictions. While all three transferred pre-trained CNN models performed well (above 98% overall classification accuracy), MobileNetV3Small is the recommended model for this classification problem due to its significantly less computationally demanding characteristics. Transferred pre-trained CNN based on MobileNetV3Small yielded Accuracy, Recall, Specificity, Precision, F1 Score, and Area Under the ROC Curve (AUC) of 0.982 ± 0.011, 0.973 ± 0.027, 0.99 ± 0.013, 0.989 ± 0.015, 0.981 ± 0.012 and 0.982 ± 0.012 respectively, averaged over the five folds on the unseen dataset. Promising results show that the developed models have the potential to provide high-quality support to haematologists by expertly performing tedious, repetitive, and time-consuming tasks in hospitals and remote/low-resource settings.INDEX TERMS Deep learning, dengue fever, digital pathology, explainable artificial intelligence, GradCAM, peripheral blood smear, pre-trained CNN, transfer learning.

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“…Given A k as the k-th feature map of a convolutional layer, and y c as the score for the target class c before the softmax layer, the neuron importance weight α c k is calculated by [55]:…”

Section: Ablation Studymentioning

confidence: 99%

“…where Z is the total number of pixels in the feature map A k . The Grad-CAM L c for class c is then computed as [55]:…”

Section: Ablation Studymentioning

confidence: 99%

Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism

Raha,

Gain,

Debnath

et al. 2024

IEEE Access

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In the wake of COVID-19, rising monkeypox cases pose a potential pandemic threat. While less severe than COVID-19, its increasing spread underscores the urgency of early detection and isolation to control the disease. The main difficulty in diagnosing monkeypox arises from its prolonged diagnostic process and symptoms that are similar to those of other skin diseases, making early detection and isolation challenging. To address this, the deployment of deep learning models on edge devices presents a viable solution for the rapid and accurate detection of monkeypox. However, the resource constraints of edge devices require the use of lightweight deep learning models. The limitation of these models often involves a trade-off with accuracy, which is unacceptable in the context of medical diagnostics. Therefore, the development of optimized deep learning models that are both resource-efficient for edge computing and highly accurate becomes imperative. To this end, an attention-based MobileNetV2 model for monkeypox detection, capitalizing on the inherent lightweight design of MobileNetV2 for effective deployment on edge devices, is proposed. This model, enhanced with both spatial and channel attention mechanisms, is tailored for rapid and early-stage diagnosis of monkeypox with better accuracy. We significantly improved the Monkeypox Skin Images Dataset (MSID) by incorporating a broader range of classes for similar skin diseases, thereby substantially enriching and diversifying the training dataset. This helps better distinguish monkeypox from other similar skin diseases, particularly in its early stages or when a detailed medical examination is unavailable. To ensure transparency and interpretability, we incorporated Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) to provide clear insights into the model's diagnostic reasoning. Finally, to comprehensively assess the performance of our model, we employed a range of evaluation metrics, including Cohen's Kappa, Matthews Correlation Coefficient, and Youden's J Index, alongside traditional measures like accuracy, F1-score, precision, recall, sensitivity, and specificity. The attention-based MobileNetV2 model demonstrated impressive results, outperforming the baseline models by achieving 92.28% accuracy in the extended MSID dataset, 98.19% in the original MSID dataset, and 93.33% in the Monkeypox Skin Lesion Dataset (MSLD) dataset.

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Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization

Cited by 36 publications

References 47 publications

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells

An Explainable Artificial Intelligence Integrated System for Automatic Detection of Dengue From Images of Blood Smears Using Transfer Learning

Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism

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