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

Al-Tam, Riyadh M.; Al-Hejri, Aymen M.; Narangale, S M; Mahmoud, Noha F.; Al‐masni, Mohammed A.; Al-antari, Mugahed A.

doi:10.3390/biomedicines10112971

Cited by 32 publications

(19 citation statements)

References 44 publications

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“…The performance of the DL-based model can also be enhanced by applying data treatment/pre-processing steps, as reported in earlier studies, in many applications, such as for human activity recognition [21], breast cancer classification [22], and dental biometrics [23]. Therefore, in this study, we suggested that applying data preprocessing will help in improving the performance of glaucoma detection/classification.…”

Section: Introductionmentioning

confidence: 66%

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

et al. 2022

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Recently, the development of a rapid detection approach for glaucoma has been widely proposed to assist medical personnel in detecting glaucoma disease thanks to the outstanding performance of artificial intelligence. In several glaucoma detectors, cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) play roles as the major objects that are used to analyze glaucoma. However, using CDR and DDLS is quite difficult since every person has different characteristics (shape, size, etc.) of the optic disc and optic cup. To overcome this issue, we proposed an alternative way to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL). Our proposed method is divided into two processes: (1) the pre-treatment process and (2) the glaucoma classification process. We started the pre-treatment process by removing unnecessary parts, such as the optic disc and blood vessels. Both parts are considered for removal since they might be obstacles during the analysis process. For the classification stages, we used nine deep-learning architectures. We evaluated our proposed method in the ORIGA dataset and achieved the highest accuracy of 92.88% with an AUC of 89.34%. This result is improved by more than 15% from the previous research work. Finally, it is expected that our model could help improve eye disease diagnosis and assessment.

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Section: Introductionmentioning

confidence: 66%

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

et al. 2022

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“…Their approach combines attention modules, ASPP, and feature transformer layers into a 3D U-Net architecture, which overcomes the challenges of 3D segmentation, such as varying sizes and low data quality in ABVS data. Al-Tam et al [29] presented a novel hybrid deep learning system for computer-aided diagnosis (CAD) of breast lesions. Their approach combines a residual convolutional network with a transformer encoder that incorporates multiple layer perceptron (MLP) modules.…”

Section: Transformermentioning

confidence: 99%

SaTransformer: Semantic‐aware transformer for breast cancer classification and segmentation

Zhang

Hua

et al. 2023

IET Image Processing

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Breast cancer classification and segmentation play an important role in identifying and detecting benign and malignant breast lesions. However, segmentation and classification still face many challenges: 1) The characteristics of cancer itself, such as fuzzy edges, complex backgrounds, and significant changes in size, shape, and intensity distribution make accurate segment and classification challenges. 2) Existing methods ignore the potential relationship between classification and segmentation tasks, due to the classification and segmentation being treated as two separate tasks. To overcome these challenges, in this paper, a novel Semantic‐aware transformer (SaTransformer) for breast cancer classification and segmentation is proposed. Specifically, the SaTransformer enables doing the two takes simultaneously through one unified framework. Unlike existing well‐known methods, the segmentation and classification information are semantically interactive, reinforcing each other during feature representation learning and improving the ability of feature representation learning while consuming less memory and computational complexity. The SaTransformer is validated on two publicly available breast cancer datasets – BUSI and UDIAT. Experimental results and quantitative evaluations (accuracy: 97.97%, precision: 98.20%, DSC: 86.34%) demonstrate that the SaTransformer outperforms other state‐of‐the‐art methods.

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“…They achieved AUC of 0.784. Al-Tam et al [2] proposed a new hybrid model that involved a transformer encoder with multiple layer perceptron (MLP) for classification based on the high-level deep features extracted via ResNet50. Their proposed model outperformed against others individual classification models of ResNet50, VGG16, and Custom CNN.…”

Section: Vision Transformer-based Medical Image Classificationmentioning

confidence: 99%

“…The model can respond to input from numerous representation subspaces at various locations simultaneously due to the multi-head attention. The multihead attention linearly extends the queries, keys, and values h times using a variety of learnt linear projections, and can be calculated by Equation (2).…”

Section: The Proposed Hybrid Ai Modelmentioning

confidence: 99%

“…There are a variety of risk factors that can lead to the development of this cancer, including sex, family history, aging, gene mutations, estrogen, and so on. However, there is no guarantee that any of these factors can show accurate proof of breast cancer incidence [2]. Breast cancer is always a silent disease and appears suddenly if there is no routine check annually by the patients.…”

Section: Introductionmentioning

confidence: 99%

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ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

et al. 2022

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Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies.

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

Cited by 32 publications

References 44 publications

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

SaTransformer: Semantic‐aware transformer for breast cancer classification and segmentation

ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

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