A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks

Altaf, Meteb

doi:10.3934/mbe.2021256

Cited by 21 publications

(8 citation statements)

References 39 publications

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“…Primary liver cancer usually develops from cirrhotic regenerative nodules (RN) to dysplastic nodules (DN) and eventually progresses to early HCC [ 1 ]. The phenotype of RN is normal in nature and is generally considered to be benign, whereas DN has some features similar to well-differentiated HCC, such as disorganized arteries, clonal-like features, and varying degrees of cellular atypia [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced Ultrasound and Magnetic Resonance Enhancement Based on Machine Learning in Cancer Diagnosis in the Context of the Internet of Things Medical System

Zhou

Zhang

You

et al. 2022

Computational Intelligence and Neuroscience

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As an accurate, safe, and effective noninvasive examination method, imaging examination has been widely used in the diagnosis and differential diagnosis of focal liver lesions. Enhanced ultrasonography (CEUS), enhanced CT (CECT), and enhanced magnetic resonance imaging (CEMRI) are the most commonly used enhanced imaging methods in clinical practice, all of which can accurately determine the nature of liver lesions. The purpose of this paper is to study the application of contrast-enhanced ultrasound and magnetic resonance enhancement in cancer diagnosis based on the Internet of Things medical system. The basic clinical data, CEUS, and enhanced CT/MRI findings of 120 CHC patients were retrospectively analyzed. The clinicopathological features of CHC patients were investigated by contrast-enhanced ultrasonography and CT/MRI enhanced mode. The diagnostic value of contrast-enhanced ultrasound and enhanced CT/MRI combined with tumor markers in CHC was analyzed. The experimental results showed that the sensitivities of CEUS, enhanced MRI, and their combination in diagnosing CHC were 72.44%, 81.56%, and 93.78%, respectively. This experiment has an important value in the diagnosis of primary liver cancer.

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Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced Ultrasound and Magnetic Resonance Enhancement Based on Machine Learning in Cancer Diagnosis in the Context of the Internet of Things Medical System

Zhou

Zhang

You

et al. 2022

Computational Intelligence and Neuroscience

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“…To further demonstrate the superiority of the proposed model, comparison results with existing algorithms on MIAS dataset are given in Table 12 . The literature ( 32 ) compared the classification results achieved by using different classifiers and preprocessing method of dataset splitting; the literature ( 33 ) proposed a fuzzy multilayer classifier (FMSVM) model; the literature ( 34 ) proposed a CNN-based computer detection system, which contains 8 convolutional layers, 4 maximum pooling layers and 2 fully connected layers; literature ( 35 ) built a hybrid model based on pulse-coupled neural network (PCNN) and CNN; literature ( 36 ) proposed a RANSAC model based on image processing for pectoral muscle detection method and used U-Net architecture to train the model; literature ( 37 ) proposed Morph-SPCNN model to solve the limitations of over-segmentation of mammographic images by employing SVM incorporating Gaussian, linear and polynomial kernels as classifiers; literature ( 38 ) proposed a texture based associative classifier (TBAC) for automatic breast cancer classification system; literature ( 39 ) used halarick’s texture feature extraction algorithm to obtain GLCM from mammographic images, and proposed a three-class classification of mammographic images based on the watershed algorithm combined with the K-NN classifier. By observing the experimental results in Table 12 , it is easy to find that the recognition results of the proposed algorithm (98.79%) is better than the existing algorithms mentioned above.…”

Section: Discussionmentioning

confidence: 99%

Self-attention random forest for breast cancer image classification

Shi

Chen

et al. 2023

Front. Oncol.

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IntroductionEarly screening and diagnosis of breast cancer can not only detect hidden diseases in time, but also effectively improve the survival rate of patients. Therefore, the accurate classification of breast cancer images becomes the key to auxiliary diagnosis.MethodsIn this paper, on the basis of extracting multi-scale fusion features of breast cancer images using pyramid gray level co-occurrence matrix, we present a Self-Attention Random Forest (SARF) model as a classifier to explain the importance of fusion features, and can perform adaptive refinement processing on features, thus, the classification accuracy can be improved. In addition, we use GridSearchCV technique to optimize the hyperparameters of the model, which greatly avoids the limitation of artificially selected parameters.ResultsTo demonstrate the effectiveness of our method, we perform validation on the breast cancer histopathological image-BreaKHis. The proposed method achieves an average accuracy of 92.96% and a micro average AUC value of 0.9588 for eight-class classification, and an average accuracy of 97.16% and an AUC value of 0.9713 for binary classification on BreaKHis dataset.DiscussionFor the sake of verify the universality of the proposed model, we also conduct experiments on MIAS dataset. An excellent average classification accuracy is 98.79% on MIAS dataset. Compared to other state-of-the-art methods, the experimental results demonstrate that the performance of the proposed method is superior to that of others. Furthermore, we can analyze the influence of different types of features on the proposed model, and provide theoretical basis for further optimization of the model in the future.

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“…The proposed hybrid model was tested on a previously unreported MIAS dataset and showed an accuracy of 98.7%. In the Results section, further assessment measures can be found [ 25 ]. There are a variety of digital pathology image-evaluation techniques for breast cancer, including rule-based and machine-learning approaches [ 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning

Kaur,

Kaushal,

Sandhu

et al. 2023

Diagnostics

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Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

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A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks

Cited by 21 publications

References 39 publications

Contrast-Enhanced Ultrasound and Magnetic Resonance Enhancement Based on Machine Learning in Cancer Diagnosis in the Context of the Internet of Things Medical System

Contrast-Enhanced Ultrasound and Magnetic Resonance Enhancement Based on Machine Learning in Cancer Diagnosis in the Context of the Internet of Things Medical System

Self-attention random forest for breast cancer image classification

Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning

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