Breast Tumor Classification Using Fast Convergence Recurrent Wavelet Elman Neural Networks

Boldbaatar, Enkh-Amgalan; Lin, Lo-Yi; Lin, Chunshui

doi:10.1007/s11063-018-9931-4

Cited by 4 publications

(4 citation statements)

References 16 publications

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Section: Resultsmentioning

confidence: 99%

“…The convolutional neural network model proposed is a black-box model in which the processing and diagnosis process cannot be inferred. To figure out how the model assists in the diagnosis, the Grad-CAM method [ 14 , 15 , 16 , 17 ] is used in this study to perform a visual analysis on the classification of model diagnoses. Grad-CAM has been successfully used to provide the interpretability of the model for medical images, such as MRI and endoscopic images [ 16 , 17 ].…”

Section: Resultsmentioning

confidence: 99%

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Deep learning for differentiating benign from malignant tumors on breast-specific gamma image

Lei

Dong

Yang

et al. 2023

THC

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BACKGROUND: Breast diseases are a significant health threat for women. With the fast-growing BSGI data, it is becoming increasingly critical for physicians to accurately diagnose benign as well as malignant breast tumors. OBJECTIVE: The purpose of this study is to diagnose benign and malignant breast tumors utilizing the deep learning model, with the input of breast-specific gamma imaging (BSGI). METHODS: A benchmark dataset including 144 patients with benign tumors and 87 patients with malignant tumors was collected and divided into a training dataset and a test dataset according to the ratio of 8:2. The convolutional neural network ResNet18 was employed to develop a new deep learning model. The model proposed was compared with neural network and autoencoder models. Accuracy, specificity, sensitivity and ROC were used to evaluate the performance of different models. RESULTS: The accuracy, specificity and sensitivity of the model proposed are 99.1%, 98.8% and 99.3% respectively, which achieves the best performance among all methods. Additionally, the Grad-CAM method is used to analyze the interpretability of the diagnostic results based on the deep learning model. CONCLUSION: This study demonstrates that the proposed deep learning method could help physicians diagnose benign and malignant breast tumors quickly as well as reliably.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Deep learning for differentiating benign from malignant tumors on breast-specific gamma image

Lei

Dong

Yang

et al. 2023

THC

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“…Eqs. ( 11), (12), and (13) show that energy in the spatial domain is equal to the energy in the frequency domain [35].…”

Section: Scattering (I Order)mentioning

confidence: 99%

“…Three datasets: the Raabin white blood cell dataset (RWBCD), leukocyte images for segmentation and classification (LISC), and the blood cell count dataset (BCCD) were used to validate the method. An intelligent classification approach using an Elman neural network was put forth [13] to classify healthy and unhealthy cells. The network used various wavelet functions at different hidden layers to increase generalization and search space compared to a simple neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Channel Fusion Filter and Invariant Scattering Network-Based Leukocyte Image Discrimination Framework

2024

IJIES

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The escalating demand for accessible, effective, and precise healthcare solutions has driven extensive research into integrating artificial intelligence into the medical field. Recognizing intricate patterns within biomedical images remains a formidable challenge for human practitioners. In response to this, our study introduces a pioneering image enhancement paradigm, the stationary wavelet channel fusion filtered (SWCFF) algorithm. Additionally, we employ the invariant scattering network (ISN), a novel feature learning methodology to analyze leukocyte images. The novelty of our approach lies in the inventive combination of SWCFF and ISN for enhanced feature extraction and discrimination of leukocytes. Our investigation aims to assess the efficacy of automatically extracted features from this algorithm in differentiating leukocytes, leveraging a support vector machine (SVM) classifier for the diagnosis and detection of leukemia and other blood-related conditions. The proposed model is rigorously evaluated on four benchmark datasets: ALLIDB, C_NMC, BCCD, and LISC. Notably, the ALLIDB binary class achieves a peak accuracy of 96.15% (95% CI: 0.9 to 1), while the class accuracy for neutrophils reaches an impressive 97.96% (95% CI: 0.89 to 1), accompanied by a precision of 100% and a false positive rate of 0%. Our innovative approach holds promise for the deployment of a cost-effective computer-aided diagnosis (CAD) tool in rural settings, aiding physicians in early disease prediction and the timely monitoring of treatment.

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Intelligent Multimedia Applications in Wavelet Domain: New Trends and Future Research Directions

Singh

Nigam

Singh

et al. 2020

Intelligent Wavelet Based Techniques for Advanced Multimedia Applications

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Breast Tumor Classification Using Fast Convergence Recurrent Wavelet Elman Neural Networks

Cited by 4 publications

References 16 publications

Deep learning for differentiating benign from malignant tumors on breast-specific gamma image

Deep learning for differentiating benign from malignant tumors on breast-specific gamma image

Channel Fusion Filter and Invariant Scattering Network-Based Leukocyte Image Discrimination Framework

Intelligent Multimedia Applications in Wavelet Domain: New Trends and Future Research Directions

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