scholarly journals Techniques for Lung Cancer Detection from CT Image

2019 ◽  
Vol 9 (2S) ◽  
pp. 653-657
Keyword(s):  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Medical Field ◽  
Huge Number ◽  
X Ray ◽  
Lung Cancer Detection ◽  
Detailed Diagnosis

The most lethal disease found in the medical field is lung cancer and early detection of this disease has become a challenge for many doctors and diagnostics. The lung cancer contributes over 15.3% of the total number of new cases diagnosed in the recent years. Smoking and pollution are considered as the major causes of lung cancer. At present, there are huge number of tests available to detect lung cancer such as PET Scan, Computerized Tomography (CT) Scan and X-ray etc. are used to diagnose the disease. By x-ray the picture of the lungs may uncover the unusual mass or nodule. A further developed adaption found in CT scan which can uncover the small lesions in the lung that probably won’t be distinguished with X-ray. Biopsy tests are done for detailed diagnosis of the disease. For accurate and better results, a data mining techniques, machine learning algorithms or deep learning algorithms could be used in the laboratories. In this survey, we have elaborated various existing techniques used so far.

scholarly journals Lung Cancer Detection Using Chi-Square Feature Selection and Support Vector Machine Algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 2050-2060
Keyword(s):  
Lung Cancer ◽  
Feature Selection ◽  
Cancer Detection ◽  
Execution Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Chi Square ◽  
Cancer Dataset ◽  
Lung Cancer Detection

Lung Cancer is the most general type of disease in theworld ofcancer. It affects the lungs of the human body. So, the prediction of lung cancer at its earlier stage is difficult. It is the deadliest cause of death in both men and women. Its symptoms are harder to recognize in the initial stages.Machine learning algorithms have made the prediction and detection of lung cancereasier. Chi-square is used for feature selection to select the relevant features in the lung cancer dataset. Different Machine Learning algorithms are used to predict Lung Cancer.The algorithmsutilized in the proposed work are SVM and Random Forest. We have compared these algorithms with and without feature selection (Chi-square). SVM is identified as the best algorithm in the proposed work due to its accuracy and less execution time for detecting the model. The key objective of this paper is to enhance the accuracy and reduce the execution time of the classifier.

scholarly journals Certain Investigations on the Application of Machine learning Algorithms and Deep Learning Architectures for Covid -19 Diagnosis

2021 ◽  
Author(s):  
Soundariya R.S. ◽  
◽  
Tharsanee R.M. ◽  
Vishnupriya B ◽  
Ashwathi R ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mathematical Models ◽  
Ct Scan ◽  
Virus Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Laboratory Test Results ◽  
Learning Architectures

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.

scholarly journals Lung Cancer Detection Based on CT Scan Images by Using Deep Transfer Learning

2019 ◽  
Vol 36 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Tulasi Sajja ◽  
Retz Devarapalli ◽  
Hemantha Kalluri
Keyword(s):  
Lung Cancer ◽  
Ct Scan ◽  
Transfer Learning ◽  
Cancer Detection ◽  
Lung Cancer Detection
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