Lung Cancer Detection from LDCT Images Using Deep Convolutional Neural Networks

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.

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Lung Cancer Detection using 3D Convolutional Neural Networks

2020 International Conference on Computational Performance Evaluation (ComPE) ◽

10.1109/compe49325.2020.9200176 ◽

2020 ◽

Author(s):

Adarsh Pradhan ◽

Bhaskarjyoti Sarma ◽

Bhiman Kr Dey

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Cancer Detection ◽

Lung Cancer Detection

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i1.pp126-133 ◽

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 {10-8, 10-4, 10-3, 5x10-2, 10-1, 1, 104} 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.

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