Lung cancer is a dangerous disease with differing treatment plans based on types and location of the cancerous cells. The overall 5-year survival rate for all stages of lung cancer is around 15%. People who smoke are ...
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Lung cancer is a dangerous disease with differing treatment plans based on types and location of the cancerous cells. The overall 5-year survival rate for all stages of lung cancer is around 15%. People who smoke are at the highest risk of developing lung cancer. Early detection of lung cancer is crucial for starting early treatment and preventing the disease from spreading. Hence, it can improve people’s chances of survival. Imaging tests, such as a chest computed tomography (CT) scan, can detect lung cancer by providing a more detailed picture. However, the examination of chest CT scans is a challenging task and is prone to subject variability. For this, researchers have developed many computer-aided diagnostic (CAD) systems for the automatic detection of cancer using CT scan images. Misdiagnoses can occur in manual interpretation of images. An automated trained neural network on lung images from healthy and malignant lung cells helps lower the problem. Convolutional neural network (CNN)-based pretrained deep learning models have been used successfully to detect lung cancer. The accuracy of classification is significant to avoid false prediction. This research presents a metalearning based approach for identifying the common types of lung cancer tissues namely, Benign tissue, Squamous Cell Carcinoma, and Adenocarcinoma using LC25000 dataset. All the experiments have been conducted on a publicly available benchmark dataset for lung histopathological images. The features extracted from the penultimate layer (global average pooling) of the transfer learning-based CNN models, namely InceptionResNetV1, EfficientNetB7, and DenseNet121, have been fused together, and the dimensionality reduction has been applied to them before passing to the metaclassifier, which is the Support Vector Machine (SVM) classifier in our case. A quantitative analysis of the proposed algorithm has been conducted through classification accuracy and confusion matrix computation. When compared wit
Cloud computing makes computers a utility and allows scientific, consumer, and corporate applications. This implementation raises energy, CO2, and economic problems. Cloud computing companies are concerned about energ...
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The economic growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the primary source of income and its contributing 17% to the total GD...
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The facts allied to the interaction of proteins are crucial due to their significance in numerous biological and cellular activities. With the accessibility of huge protein-protein interaction datasets, the openings t...
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Malware, a form of harmful software, poses a significant threat to victims by compromising data integrity and facilitating unauthorized access. Analogous to the COVID virus's impact on the human body, untreated ma...
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Due to quality degradations induced at various phases of visual signal capture, compression, transmission, and display, perceptual quality evaluation is crucial in visual communication systems. This work addresses the...
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In this paper, we introduce the Maximum Distance Sublattice Problem (MDSP). We observed that the problem of solving an instance of the Closest Vector Problem (CVP) in a lattice L is the same as solving an instance of ...
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With the rapid growth of online examination platforms, maintaining high levels of security, integrity, and user authentication is paramount. While existing methods utilize traditional security measures, the integratio...
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Bullying today is defined as repeated and intentional hostile actions by a powerful person against one more vulnerable. It frequently occurs online, due to the proliferation of digital devices and widespread internet ...
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Data mining has applications for a wide range of enterprises, including those in the banking, telecommunications, energy, medical, marketing, and finance industries. The real-world situations involve noisy, unpredicta...
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