Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

Ramana, Kadiyala; Kumar, Madapuri Rudra; Sreenivasulu, K.; Gadekallu, Thippa Reddy; Bhatia, Surbhi; Agarwal, Parul; Idrees, Sheikh Mohammad

doi:10.3389/fonc.2022.886739

Cited by 27 publications

(10 citation statements)

References 41 publications

(40 reference statements)

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“…Methodology. By utilizing the Adam optimizer and comparing it to VGG16 [27] and Inception v3, the suggested technique produce the result with excellent accuracy while 2.1.1. Normalization.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.

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Section: Related Workmentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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“…An invention patent [27] and system had been used since 2012 until now in one of Taiwan's medical centers. To continuously improve research methods, dimensionality reduction techniques [28] will be used in future research.…”

Section: Discussionmentioning

confidence: 99%

Establishing a Prediction Model by Machine Learning for Accident-Related Patient Safety

Huang

Tseng

Chu

2022

Wireless Communications and Mobile Computing

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Patient safety has always been an important issue when improving the quality of medical care. The first step when preventing accidents is to screen for the high-risk groups that are prone to accidents. A patient safety reporting system is one of the best tools for such screening. We used machine learning techniques to analyze events involving falling and establish a risk prediction model. The results are then fed back to medical organizations with the aim of raising their quality of medical care. Bayesian network, artificial neural network, logistic regression, and decision tree-based chi-square automatic interaction detection were applied to analyze a database covering a 14-month period from November 2012 to December 2013, and the area under the ROC curve (AUC) values were 0.940, 0.979, 0.981, and 0.971, respectively. Next, data from January to February 2013 was verified by the model showing the highest discrimination ability, namely, logistic regression, and the AUC and F -measure values were 0.944 and 0.871, respectively. Our results demonstrate that a high level of performance was obtained when using a machine learning model for fall prediction among newly admitted patients. Machine learning can thus be applied to provide the most appropriate decision-aided prediction model when screening high-risk groups that are prone to accidents within hospitals.

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“…The Batch Normalization (BN) layer allows each layer of the architectural model to undertake more autonomous learning [ 37 ]. This layer's primary function is to normalize the output of the layer previously.…”

Section: Background On Cnnmentioning

confidence: 99%

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Tiwari

Pant

Elarabawy

et al. 2022

Computational Intelligence and Neuroscience

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Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

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Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

Cited by 27 publications

References 41 publications

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

Establishing a Prediction Model by Machine Learning for Accident-Related Patient Safety

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

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