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

Sajja, Tulasi; Devarapalli, Retz; Kalluri, Hemantha Kumar

doi:10.18280/ts.360406

Cited by 83 publications

(32 citation statements)

References 16 publications

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“…The study design was a cross-sectional study that uses 2 data sources: 1) LUNA16 dataset, 23 and 2) ChestRama dataset. The LUNA16 was constructed by the public lung CT scan database namely LIDC/IDRI 24 which aimed to classify nodule to either benign or malignancy. The number of samples in LUNA16 is 8106 cropped nodule images; 6755 (83.33%) images of benign nodules, and 1351 (16.67%) images of malignant nodules.…”

Section: Methodsmentioning

confidence: 99%

NoduleNet: A Lung Nodule Classification Using Deep Learning

et al. 2020

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Background: Accurate detection and classification of lung nodules at an early stage can help physicians to improve the treatment outcomes of lung cancer. Several lung nodule classifications using deep learning have been proposed but they are lag of external validation to Thai patient data. Objective: To propose a deep learning model called NoduleNet for lung nodule classification and perform internal and external validation of the proposed model. Methods: Two datasets were performed; internal validation using LUNA16 (the public lung CT database), and external validation using ChestRama (37 chest CT scans retrospectively identified from the CT database of Ramathibodi Hospital between 2017 and 2019). The NoduleNet was built on top of pretrained architecture, VGG16, and VGG19 with customization. Results: The NoduleNet showed impressive results in nodule classification. The best model achieved accuracy of 0.95 (0.94 - 0.96), sensitivity of 0.84 (0.82 - 0.86), and specificity of 0.97 (0.97 - 0.98) for internal validation, where the external validation results was accuracy of 0.95 (0.87 - 1.00), sensitivity of 0.91 (0.82 - 1.00), and specificity of 1.00 (1.00 - 1.00). There were 3 misclassified samples in external validation which are all false-negative. Conclusions: The NoduleNet is able to generalize from non-Thai patient data to Thai patient data. It could be further improved by taking sequence of images into account, integrating with an automatic nodule detection algorithm, and adding more nodule types.

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Section: Methodsmentioning

confidence: 99%

NoduleNet: A Lung Nodule Classification Using Deep Learning

et al. 2020

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“…ResNet18, Googlenet, and ResNet50) respectively are used learning curve and loss function i.e., the loss function is a fault prediction of the model's performance; as we notice in Figure 8, 9, 10 and 11, the loss curve tends to zero through every epoch. [21] Used AlexNet GoogleNet ResNet50 and densely connected architecture they tested on 80% training and 20% testing dataset samples they got 100 %, 98.84 %, 100 %, and 100 % accuracy respectively.…”

Section: Evaluation Matrixmentioning

confidence: 99%

Lung cancer classification with Convolutional Neural Network Architectures

Mohammed

Çınar

2021

QAJ

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One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cancer is the early stage. However, the early stage of lung cancer often does not have any clinical symptoms and is difficult to be found. In this paper, lung nodule classification has been performed; the data have used of CT image is SPIE AAPM-Lung. In recent years, deep learning (DL) was a popular approach to the classification process. One of the DL approaches that have used is Transfer Learning (TL) to eliminate training costs from scratch and to train for deep learning with small training data. Nowadays, researchers have been trying various deep learning techniques to improve the efficiency of CAD (computer-aided system) with computed tomography in lung cancer screening. In this work, we implemented pre-trained CNN include: AlexNet, ResNet18, Googlenet, and ResNet50 models. These networks are used for training the network and CT image classification. CNN and TL are used to achieve high performance resulting and specify lung cancer detection on CT images. The evaluation of models is calculated by some matrices such as confusion matrix, precision, recall, specificity, and f1-score.

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“…A deep convolutional neural network [18,19] has been proposed to classify the input MRI image with more accuracy. The structure of the network is depicted in Figure 4.…”

Section: Proposed Cnn Modelmentioning

confidence: 99%

Classification of Brain Tumors Using Convolutional Neural Network over Various SVM Methods

Sajja¹,

Kalluri²

2020

ISI

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A computer-based method is presented in this paper to define brain tumor using MRI images. The main classification motive is to identify a brain into a healthy brain or classify a brain with a tumor when a patient's MRI images are given. Magnetic Resonance Imaging (MRI) is an important one among the common imaging treatments, which presents more detailed brain tumor identification information and provides detailed pictures of inside your body other than computed tomography (CT). Currently, CNNs is a famous technique to deal with most of the problems with image classification as they provide greater accuracy compared to other classifiers. Hbridized CNN has been used in this work. It consists of three convolution layers and three max pooling layers which could provide outrated performance. Images from open databases such as BRATS were tested on brain MRI images. The proposed model has given the improved performance over the existing model with an accuracy of 96.15%.

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

Cited by 83 publications

References 16 publications

NoduleNet: A Lung Nodule Classification Using Deep Learning

NoduleNet: A Lung Nodule Classification Using Deep Learning

Lung cancer classification with Convolutional Neural Network Architectures

Classification of Brain Tumors Using Convolutional Neural Network over Various SVM Methods

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