scholarly journals Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review

Cureus ◽  
2020 ◽  
Author(s):  
Kaviya Sathykumar ◽  
Michael Munoz ◽  
Jaikaran Singh ◽  
Nowair Hussain ◽  
Benson A Babu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Neural Networks ◽  
Literature Review ◽  
Convolutional Neural Networks ◽  
Cancer Detection ◽  
Deep Convolutional Neural Networks ◽  
Narrative Literature ◽  
Narrative Literature Review ◽  
Lung Cancer Detection

scholarly journals “ EARLY PREDICTION OF LUNG CANCER DETECTION USING EXTENDED CONVOLUTIONAL NEURAL NETWORKS”

2021 ◽  
Vol 58 (1) ◽  
pp. 5614-5624
Author(s):  
Dr. Asadi Srinivasulu, Dr. Umesh Neelakantan, Tarkeshwar Barua
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cancer Detection ◽  
Computational Cost ◽  
Malignant Growth ◽  
Huge Number ◽  
Threat Level ◽  
Precision Time ◽  
Lung Cancer Detection

Lung disease is one of the significant reasons for malignancy related passing because of its forceful nature and postponed discoveries at cutting edge stages. Early discovery of disease would encourage in sparing a huge number of lives over the globe consistently. Lung malignant growth discovery at beginning time has gotten significant and furthermore simple with picture handling and profound learning systems. Lung Cancer side effects are persistent cough, chest torment that deteriorates with profound breathing, roughness, unexplained loss of hunger and weight, coughing up blood or rust-shaded mucus, brevity of breath, bronchitis, pneumonia or different diseases that continue repeating. Lung quiet Computer Tomography (CT) check pictures are utilized to identify and arrange the lung knobs and to recognize the threat level of that knob. Extended Convolutional Neural Networks (ECNN) work achieved relative examination with parameters like precision, time intricacy and elite, lessens computational cost, and works with modest quantity of preparing information is superior to the current framework. consumers.

scholarly journals A hybrid model based on convolutional neural networks and fuzzy kernel K-medoids for lung cancer detection

2021 ◽  
Vol 24 (1) ◽  
pp. 126
Author(s):  
Glori Stephani Saragih ◽  
Zuherman Rustam ◽  
Jane Eva Aurelia
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Model ◽  
Cancer Detection ◽  
High Performance ◽  
Correct Diagnosis ◽  
Computational Time ◽  
Model Based ◽  
Lung Cancer Detection

Lung cancer is the deadliest cancer worldwide. Correct diagnosis of lung cancer is one of the main tasks that is challenging tasks, so the patient can be treated as soon as possible. In this research, we proposed a hybrid model based on convolutional neural networks (CNN) and fuzzy kernel k-medoids (FKKM) for lung cancer detection, where the magnetic resonance imaging (MRI) images are transmitted to CNN, and then the output is used as new input for FKKM. The dataset used in this research consist of MRI images taken from someone who had lung cancer with the treatment of anti programmed cell death-1 (anti-PD1) immunotherapy in 2016 that obtained from the cancer imaging archive. The proposed method obtained accuracy, sensitivity, precision, specificity, and F1-score 100% by using radial basis function (RBF) kernel with sigma of {10<sub>­</sub>­<sup>-8</sup>, 10<sub>­</sub>­<sup>-4</sup>, 10<sub>­</sub>­<sup>-3</sup>, 5x10<sub>­</sub>­<sup>-2</sup>, 10<sub>­</sub>­<sup>-1</sup>, 1, 10­­<sup>4</sup>} in 20-fold cross-validation. The computational time is only taking less than 10 seconds to forward dataset to CNN and 3.85 ± 0.6 seconds in FKKM model. So, the proposed method is more efficient in time and has a high performance for detecting lung cancer from MRI images.

scholarly journals Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

Heliyon ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e06626
Author(s):  
Paulina Cecula ◽  
Jiakun Yu ◽  
Fatema Mustansir Dawoodbhoy ◽  
Jack Delaney ◽  
Joseph Tan ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Literature Review ◽  
Patient Flow ◽  
Narrative Literature ◽  
Narrative Literature Review ◽  
Inpatient Units ◽  
Improve Patient

scholarly journals Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Holly Burrows ◽  
Javad Zarrin ◽  
Lakshmi Babu-Saheer ◽  
Mahdi Maktab-Dar-Oghaz
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
User Interface ◽  
Convolutional Neural Networks ◽  
Mental Health Problems ◽  
Well Being ◽  
Working Environment ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

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