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 “ 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 Deep Analysis of Google Net and Alex Net for Lung Cancer Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 395-399
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Neural Networks ◽  
Cancer Detection ◽  
Research Work ◽  
Imaging Diagnosis ◽  
Layered Architecture ◽  
Initial Learning ◽  
In The Beginning ◽  
Lung Cancer Detection

Lung cancer is the major cancer that cannot be disregarded intentionally and causes deceased with late healthcare. Now, Computed Tomography(CT) scan allows the doctors to recognize the lung cancer in the beginning of the stage. Majority of cases are tends to be failed in diagnosis of determining the lung cancer eventhough the doctors are experienced, they failed to detect the cancer. Deep learning is the important technique that can be applicable in medical imaging diagnosis. In this paper, the implementation of Convolutional Neural Networks such as GoogleNet (Inception) and AlexNet are analyzed for the lung cancer detection. The cancer images from LIDC-IDRI dataset is used for this research work. The Preprocessed cancer images are trained using GoogleNet and AlexNet to determine the cancer affected part of the lungs. The identification of lung cancer by using GoogLeNet and AlexNet are used for training the network, and image classification. These networks are provided with layered architecture for classification. We have found that AlexNet and GoogLeNet provides the comparable results by including parameters like time, initial learning rate and accuracy

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