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Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.

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Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed deep learning using convolution neural networks (CNNs) for the automatic detection and classification of all three categories of microcalcification. CNN was applied using four different optimizers (ADAM, ADAGrad, ADADelta, and RMSProp). The input images of a size of 299 × 299 × 3, with fully connected RELU and SoftMax output activation functions, were utilized in this study. The feature map was obtained using the pretrained InceptionResNetV2 model. The performance evaluation of our classification scheme was tested on a curated breast imaging subset of the DDSM mammogram dataset (CBIS–DDSM), and the results were expressed in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). Our proposed classification scheme outperforms the ability of previously used deep learning approaches and classical machine learning schemes.

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A SARS-CoV-2 virus has spread around the globe since March 2020. Millions of people infected worldwide with coronavirus. People from every country expressed their sentiments about coronavirus on social media. The aim of this work is to determine the general public opinion of Indian Twitter users about coronavirus. The Hindi tweets posted about COVID-19 is used as input data for sentiment analysis. The natural language processing is applied on input data for feature extraction. Further, the optimal features are selected from the pre-processed data using the metaheuristic based Grey wolf optimization technique. Finally, a hybrid of convolution neural network(CNN) and a long short-term memory (LSTM) model pair is employed to categorize the sentiments as positive, negative, and neutral. The outcome of the proposed model is compared with other machine learning techniques, namely, Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, Support vector machine (SVM), CNN, LSTM, LSTM–CNN, and CNN–LSTM. The highest accuracy of 87.75%, 88.41%, 87.89%, 85.54%, 89.11%, 91.46%, 88.72%, 91.54%, and 92.34% is obtained by Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, SVM, CNN, LSTM, LSTM–CNN, and CNN–LSTM, respectively. The proposed ensemble hybrid model gives the highest 95.54%, 91.44%, 89.63%, and 90.87% classification accuracy, precision, recall, and F-score, respectively.