Lung Cancer Classification Techniques

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Prarthana K R ◽  
Bhavani K
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Imaging Techniques ◽  
Research Work ◽  
Lung Nodule ◽  
Classification Systems ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
Ann Model ◽  
Cancer Dataset

Diagnosis of lung cancer with high accuracy rate is most difficult task to make remarkable vary in survival rate of patients. Different imaging techniques are used by radiologists and specialists to diagnose lung cancer such as Computer tomography (CT), X-ray and Magnetic Resonance Imaging (MRI). These methods help us to predict the malignant or benign or normal nodules present in the lungs. This proposed work is to build a lung classification system that can classify the images as malignant or benign or normal cases and give best accuracy for predicting lung cancer. In this “IQ_OTH/NCCD” lung cancer dataset is used which consist of total 1190 images of lung CT scans slices for 110 cases. CT scans in DICOM formats is utilized in this research work. In this proposed work by applying machine learning techniques such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), classify the malignant or normal or benign lung nodule cases and finally compare all the attained results. This work finds the accuracy of applied classification systems and finally CNN model outperforms with an accuracy of 98%. Accuracy of ANN model is observed to be 71%.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

Effective Implementation of Value Engineering Using Artificial Neural Network Aid of Optimization Techniques

2019 ◽  
Vol 27 (03) ◽  
pp. 1950022 ◽  
Author(s):  
M. Prem Swarup ◽  
A. Prabhu Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Work ◽  
Optimization Techniques ◽  
Value Engineering ◽  
Training Algorithms ◽  
Air Conditioner ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Value Engineering (VE) is a method for characterizing the developed requirements of a product, and it is concerned with the selection of less excessive conditions. VE can understand and improve the optimal outcome such as quantity, security, unwavering quality and convertibility of each managerial unit. It is an incredible solving tool that can diminish costs while preserving or improving performance and quality requirements. In this research work, VE is presented to calculate the heating cost and cooling cost of the air conditioner with the assistance of an Artificial Neural Network (ANN) with an optimization model. This ANN model effectively chooses the maximum number of sources obtainable and the source respective method with low functional cost and energy consumption. For improving the prediction accuracy of VE in the ANN model, we have incorporated some training algorithms and optimized the network hidden layer and hidden neuron by Opposition Genetic Algorithm (OGA). From the results, trained ANN with OGA predicts the output with 96.02% accuracy and also minimum errors compared with the existing GA process.

Prediction of Life of Compound Die Using Artificial Neural Network (ANN)

2014 ◽  
Vol 622-623 ◽  
pp. 664-671 ◽  
Author(s):  
Sachin Kashid ◽  
Shailendra Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sheet Metal ◽  
Research Work ◽  
Fem Simulation ◽  
Fem Analysis ◽  
Ann Model ◽  
Sheet Metal Parts ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of life of compound die is an important activity usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of compound die using artificial neural network (ANN) is presented. The parameters affecting life of compound die are investigated through FEM analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different compound dies designed for manufacturing sheet metal parts. A sample run of the proposed ANN model is also demonstrated in this paper.

Computer extracted measurements of vessel tortuosity on baseline CT scans to predict response to nivolumab immunotherapy for non-small cell lung cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11566-11566
Author(s):  
Monica Khunger ◽  
Mehdi Alilou ◽  
Rajat Thawani ◽  
Anant Madabhushi ◽  
Vamsidhar Velcheti
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Nodule ◽  
Small Cell ◽  
Ct Scans ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Vessel Tortuosity

11566 Background: Immune-checkpoint blockade treatments, particularly drugs targeting the programmed death-1 (PD-1) receptor, demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). We sought to evaluate whether computer extracted measurements of tortuosity of vessels in lung nodules on baseline CT scans in NSCLC patients(pts) treated with a PD-1 inhibitor, nivolumab could distinguish responders and non-responders. Methods: A total of 61 NSCLC pts who underwent treatment with nivolumab were included in this study. Pts who did not receive nivolumab after 2 cycles due to lack of response or progression per RECIST were classified as ‘non-responders’, patients who had radiological response per RECIST or had clinical benefit (defined as stable disease >10 cycles) were classified as ‘responders’. A total of 35 quantitative tortuosity features of the vessels associated with lung nodule were investigated. In the training cohort (N=33), the features were ranked in their ability to identify responders to nivolumab using a support vector machine (SVM) classifier. The three most informative features were then used for training the SVM, which was then validated on a cohort of N=28 pts. Results: The maximum curvature ( f1), standard deviation of the torsion ( f2) and mean curvature ( f3) were identified as the most discriminating features. The area under Receiver operating characteristic (ROC) curve (AUC) of the SVM was 0.84 for the training and 0.72 for the validation cohort. Conclusions: Vessel tortuosity features were able to distinguish responders from non-responders for patients with NSCLC treated with nivolumab. Large scale multi-site validation will need to be done to establish vessel tortuosity as a predictive biomarker for immunotherapy. [Table: see text]

Lung Nodule Classification on Computed Tomography Using Neural Networks

2020 ◽  
Vol 17 (8) ◽  
pp. 3427-3431
Author(s):  
A. Sivasangari ◽  
D. Deepa ◽  
L. Lakshmanan ◽  
A. Jesudoss ◽  
M. S. Roobini
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Lung Nodule ◽  
Ct Scanning ◽  
Detection Algorithm ◽  
Lung Cancer Diagnosis ◽  
Long Time ◽  
Neural Network Algorithm ◽  
Manual Processing ◽  
Performance Results

Lung cancer is a leading health issue and the major cause of death among all types of cancers. CT scanning is the popular method for lung cancer diagnosis detection. Manual processing of tomograms take long time for diagnosis. It is not an easy task. This complex work can also reduce the quality of diagnosis. Machine learning and neural network algorithm can be used to automatically process X-ray pictures, tomograms and PET images to detect diseases. The goal of the proposed work is to find any abnormal thing in lungs. Convolutional neural network is trained to classify abnormal area from the normal cells. The detection algorithm is designed to determine the existence of cancer in tomography images and validation, training and testing using CT images. The proposed work investigates the performance of classifier by training algorithm with morphological feature extraction. The performance results shows that proposed method achieves higher accuracy than existing methods.

Overlooking cancer: Practising cancer diagnostics in the subjunctive mood

2018 ◽  
Vol 14 (27) ◽  
Author(s):  
Michal Frumer
Keyword(s):  
Lung Cancer ◽  
Outpatient Clinic ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Ct Scans ◽  
Lung Nodules ◽  
Risk And Uncertainty ◽  
Ethnographic Fieldwork ◽  
Subjunctive Mood

The paper explores modes of uncertainty, when watching the possible development of signs of lung cancer at a lung cancer outpatient clinic. Based on ethnographic fieldwork at a clinic in Denmark, it is presented how potential signs of lung cancer, termed nodules, on people’s lungs call to be managed due to the hope and aspirations of alleviating cancer. The paper suggests that the significance of the uncertainties of lung nodules is tried out by watching the nodule with follow-up CT scans and opposed by focusing on intervention. Approaching the management of uncertainties as in a subjunctive mood in addition to a focus on cautionary but qualified guessing, the paper proposes that the physicians try out a possible but indeterminate future of cancer, to contain the prognostic and existential uncertainties by acting ‘as if’ cancer will develop. However, in this cautionary managing of cancer doubt and uncertainty, ambiguities are (re-)produced, leaving an interim certainty: This lung nodule is most likely not and may never become cancer. In this sense, the paper argues that how we as humans are practising the management of risk and uncertainty is shared across different, specific locations.

scholarly journals Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 190-197 ◽  
Author(s):  
Shudong Wang ◽  
Liyuan Dong ◽  
Xun Wang ◽  
Xingguang Wang
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Transfer Learning ◽  
Learning Strategy ◽  
Malignant Tumors ◽  
Histopathological Examination ◽  
Ct Images ◽  
Hospital Data ◽  
Cancer Dataset ◽  
Pathological Type

AbstractLung cancer is one of the most harmful malignant tumors to human health. The accurate judgment of the pathological type of lung cancer is vital for treatment. Traditionally, the pathological type of lung cancer requires a histopathological examination to determine, which is invasive and time consuming. In this work, a novel residual neural network is proposed to identify the pathological type of lung cancer via CT images. Due to the low amount of CT images in practice, we explored a medical-to-medical transfer learning strategy. Specifically, a residual neural network is pre-trained on public medical images dataset luna16, and then fine-tuned on our intellectual property lung cancer dataset collected in Shandong Provincial Hospital. Data experiments show that our method achieves 85.71% accuracy in identifying pathological types of lung cancer from CT images and outperforming other models trained with 2054 labels. Our method performs better than AlexNet, VGG16 and DenseNet, which provides an efficient, non-invasive detection tool for pathological diagnosis.

Prediction of Life of Die Block Using Artificial Neural Network

2014 ◽  
Vol 592-594 ◽  
pp. 689-693 ◽  
Author(s):  
Sachin Kashid ◽  
Shailendra Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Work ◽  
Fem Simulation ◽  
Fem Analysis ◽  
Die Design ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Important Activity

Prediction of life of die block is an important activity of die design usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of die block of compound die using artificial neural network (ANN) is presented. The parameters affecting life of die block are investigated through Finite Element Method (FEM) analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different die block. A sample run of the proposed ANN model is also demonstrated in this paper.

scholarly journals Lung Cancer Diagnosis using Transfer Learning

2021 ◽  
Vol 9 (11) ◽  
pp. 621-634
Author(s):  
Aashka Mohite
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Transfer Learning ◽  
Cancer Diagnosis ◽  
Chronic Condition ◽  
Death Rate ◽  
Ct Scans ◽  
Lung Cancer Diagnosis ◽  
Learning Architectures ◽  
Lung Ct

Lung cancer is unquestionably a lung-influencing chronic condition that significantly hampers the respiratory system. It is the second most dangerous disease which causes increase in death rate. To resolve this issue, we had planned to create a very Convolutional Neural Network using Transfer learning to specifically classify the lungs CT scans as normal, malignant, or benign in a subtle way. A dataset of 1100 lung CT scans is used for this purpose. For the most part, five Transfer Learning architectures are compared extensively in this classification such as MobileNet, VGG16, VGG19, DenseNet-201 and ResNet-101. Out of which, DenseNet-201 performed the best. The proposed strategy achieved a mean accuracy of 53 percent in the trials and 43% of  mean F1-score, mean precision and mean recall.

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