A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis

Jiang, Shancheng; Li, Huichuan; Jin, Zhi

doi:10.1109/jbhi.2021.3052044

Cited by 56 publications

(32 citation statements)

References 45 publications

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“…e hyperparameters of the competitive models are obtained from their published papers. ese competitive models are CNN [10], EDLP [9], SqueezeNet [11], LWADL [12], Unet-dCNN [13], ResNet-50 [15], FADEM [16], and Ensemble CNN [17]. Table 2 demonstrates the testing analysis of the proposed and competitive models.…”

Section: Discussionmentioning

confidence: 99%

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An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

AlZubi

Tiwari

Walia

et al. 2022

Computational Intelligence and Neuroscience

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Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve.

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Section: Discussionmentioning

confidence: 99%

“…In [12], a lightweight attention-based deep learning model (LWADL) was designed to predict eleven skin diseases. LWADL achieved better accuracy as compared to VGG19, VGG16, ResNet50, and InceptionV3.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

AlZubi

Tiwari

Walia

et al. 2022

Computational Intelligence and Neuroscience

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“…Combined with Grad-CAM and UMAP embedding methods, we further provided an intuitive visualization of the local and global feature patterns of all EMB images learned by the VGG-19 model. Unlike other applications in cancer (24,(30)(31)(32), the implementation of this new model in myocardial injury reveals ill-defined histopathological patterns in local regions, providing a guideline and attention maps for welltrained pathologists. Therefore, integrating VGG-19 with Grad-CAM and UMAP embedding methods provides an interpretive DNN model for more accurate histopathological analyses.…”

Section: Discussionmentioning

confidence: 99%

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Jiao

Yuan

Sodimu

et al. 2022

Front. Cardiovasc. Med.

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Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.

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“…V2 is a CNN model designed specifically for portable and resourceconstrained circumstances. It is founded on an upturned residual structure in which the connections of the residual structure are linked to the bottleneck layers [30]. There are 153 layers in MobileNet V2, and the size of the input layer is h × w × k, where h = 224, w = 224, and k represents the channels, three of which are in the first layer.…”

Section: Mobilenetv2-mobilenetmentioning

confidence: 99%

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Bibi¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy

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A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis

Cited by 56 publications

References 45 publications

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

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