A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

Ukwuoma, Chiagoziem C.; Qin, Zhiguang; Heyat, Md Belal Bin; Akhtar, Faijan; Bamisile, Olusola; Muad, Abdullah Y.; Addo, Daniel; Al-antari, Mugahed A.

doi:10.1016/j.jare.2022.08.021

Cited by 61 publications

(40 citation statements)

References 65 publications

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“…In the future, the used models and possibly other ones can be investigated on mixed images collected from datasets that have different intensities, such as INbreast, DDSM, and MAIS datasets, helping to find the best models that can deal with breast cancer images with different densities. We have a plan to continue improving the performance behavior and providing more interesting breast cancer prediction results using the newly impressive AI technologies such as explainable AI [ 48 , 49 , 50 ], federated learning [ 51 ], and so on. It is known that the medical images always have common characteristics that contain similarities in contextual features, and any deep learning model should be retuned again with respect to each modality.…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

et al. 2022

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Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism. The proposed CAD system has the capability to recognize breast cancer in two scenarios: Scenario A (Binary classification) and Scenario B (Multi-classification). Data collection and preprocessing, patch image creation and splitting, and artificial intelligence-based breast lesion identification are all components of the execution framework that are applied consistently across both cases. The effectiveness of the proposed AI model is compared against three separate deep learning models: a custom CNN, the VGG16, and the ResNet50. Two datasets, CBIS-DDSM and DDSM, are utilized to construct and test the proposed CAD system. Five-fold cross validation of the test data is used to evaluate the accuracy of the performance results. The suggested hybrid CAD system achieves encouraging evaluation results, with overall accuracies of 100% and 95.80% for binary and multiclass prediction challenges, respectively. The experimental results reveal that the proposed hybrid AI model could identify benign and malignant breast tissues significantly, which is important for radiologists to recommend further investigation of abnormal mammograms and provide the optimal treatment plan.

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Section: Resultsmentioning

confidence: 99%

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

et al. 2022

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“…For direct comparison using the same dataset, four deep learning models of DenseNet 201, ResNet50, Inception-V3, and Mobilenet-V2 are adopted and used. These AI models are selected to perform such direct comparison due to their promising classification performance in the research domain [ 21 , 37 , 47 , 58 , 59 , 64 , 65 ]. Such comparison is important to investigate the reliability of the proposed model with the trusted ones.…”

Section: Discussionmentioning

confidence: 99%

“…The evaluation results shown in the result section are achieved over a 5-fold cross-validation test to investigate the reliability and feasibility of the proposed BCNet. The definition of the evaluation metrics is summarized in Equations (1)–(7) [ 20 , 59 , 60 , 61 , 62 , 63 ]. True positive (TP), true negative (TN), false positive (FP), and false negative (FN) are derived via a multi-class confusion matrix for each fold test.…”

Section: Methodsmentioning

confidence: 99%

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Chola

Muaad²,

Heyat³

et al. 2022

Diagnostics

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Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.

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“…In comparison to state-of-the-art CNN in image classification evaluations, the Vision Transformer (ViT) performs well. It is one of the favourable attempts to exploit Transformer specifically on images (18; 45; 46). Despite having better performance, it has an easy-to-use modular framework that allows for wide-ranging application in multiple tasks with minimal modification.…”

Section: Related Workmentioning

confidence: 99%

Supremacy of attention based convolution neural network in classification of oral cancer using histopathological images

Deo¹,

Pal

Panigrahi

et al. 2022

Preprint

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Introduction: Oral cancer has grown to be one of the most prevalent malignant tumours and one of the deadliest diseases in emerging and low-to-middle income nations. The mortality rate can be significantly reduced if oral cancer is detected early and treated effectively. Objectives: This study proposes an effective histopathological image classification model for oral cancer diagnosis using Vision Transformer deep learning based on multi-head attention mechanism. Methods: The oral histopathological image dataset used in the study consists of 4946 images, which were categorized into 2435 images of healthy oral mucosa and 2511 images of oral squamous cell carcinoma (OSCC). In our proposed approach, along with Vision Transformer model eight pre-trained deep learning models known as Xception, Resnet50, InceptionV3, InceptionResnetV2, Densenet121, Densenet169, Densenet201 and EfficientNetB7 have been used for the comparative analysis. 90% of the images are used for training the models while the rest 10% of the images are used for testing purposes. Results: Vision Transformer model achieved the highest classification accuracy of 97.78% in comparison to other considered deep learning models. Specificity, sensitivity and ROC AUC score are recorded as 96.88%, 98.74% and 97.74% respectively. Conclusion: We found that our proposed Vision Transformer model outperforms compared to other pre-trained deep learning models, demonstrating a stronger transfer ability of the learning in histopathological image classification from the analysis of the obtained results. This method considerably lowers the cost of diagnostic testing while increasing the diagnostic effectiveness, and accuracy for oral cancer detection in patients of diverse origin.

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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

Cited by 61 publications

References 65 publications

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Supremacy of attention based convolution neural network in classification of oral cancer using histopathological images

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