Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

Kashyap, Kanchan Lata; Bajpai, Manish Kumar; Khanna, Pritee; Giakos, G.C.

doi:10.1002/cnm.2907

Cited by 21 publications

(7 citation statements)

References 46 publications

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“…Traditional CAD methods usually need to manually extract features from images [ 7 ]. These features include original features such as shape and texture [ 8 , 9 ], and the features extracted from the original features by machine learning algorithms, such as Histogram of Gradient [ 10 – 12 ], Local Binary Patter [ 13 , 14 ] and Gabor filter [ 11 , 12 ]. However, the selection and combination of features largely depend on the experience of designers, so the traditional methods have some limitations.…”

Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/ Malignancy) of two-view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.

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Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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“…Using ANN as a classifier, the results of this study achieved 99% accuracy in the image recognition process. Kashyap et al [ 26 ] used a partial differential equation adjustment process to extract the chest area, mammogram images, dark masking, and moderate filtering and map suspicious anomalies. Fuzzy c-mean clustering is applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network

Isfahani

Jannat-Dastjerdi

Eskandari

et al. 2021

Computational Intelligence and Neuroscience

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Mammography is a significant screening test for early detection of breast cancer, which increases the patient’s chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

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“…Prediction model of HIV incidence has been proposed using neural network [10]. There are many methods exiting in literature to early detection of different diseases [11, 12, 13].…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

Kumar

Singh

Dixit

et al. 2020

Preprint

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COVID-19 has emerged as global medical emergency in recent decades. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID-19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data are integrated and passed into different Machine Learning Models to check the fit. Ensemble Learning Technique,Random Forest, gives a good evaluation score on the test data. Through this technique, various important factors are recognised and their contribution to the spread is analysed. Also, linear relationship between various features is plotted through heatmap of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of COVID19, which shows good result on test data. The inferences from Random Forest feature importance and Pearson Correlation gives many similarities and some dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.

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Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

Cited by 21 publications

References 46 publications

New convolutional neural network model for screening and diagnosis of mammograms

New convolutional neural network model for screening and diagnosis of mammograms

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network

Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

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