Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model

Wang, Cheng; Chen, Delei; Lin, Hao; Liu, Xuebo; Zeng, Yu; Chen, Jianwei; Zhang, Guokai

doi:10.1109/access.2019.2946000

Cited by 193 publications

(53 citation statements)

References 23 publications

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“…Such type of network possesses lesser number of parameters to adjust, as compared to standard convolution networks, which reduces overfitting. A recent investigation manifested that the InceptionV3 model, fine-tuned using chest X-ray films relating to the examination of pulmonary nodules, accomplished fantastic results for the diagnosis of thoracic disease, similar to the conclusion of expert radiologists [ 103 ]. Another research also applies transfer learning and deep model such as InceptionV3 on chest X-rays for the classification of pneumonia [ 66 ].Its architecture utilizes factorized inception blocks, facilitating the interface to pick appropriate kernel sizes for the convolution layers, which allows the design to gain both high- and low-level features with larger and smaller convolution layers [ 104 ].…”

Section: Resultsmentioning

confidence: 99%

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

Ahmad

Farooq

Ghani

2021

Computational Intelligence and Neuroscience

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The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.

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Section: Resultsmentioning

confidence: 99%

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

Ahmad

Farooq

Ghani

2021

Computational Intelligence and Neuroscience

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show abstract

“…A recent investigation manifested that pre-trained InceptionV3 model and fine-tuned using chest X-ray films relating to the examination of pulmonary nod-ules, accomplished fantastic results for the diagnosis of thoracic disease, similar to the conclusion of expert radiologists[ 99 ]. Another research also utilizes the InceptionV3 model and transfer learning using chest X-rays for the classifica-tion of pneumonia[ 58 ].The InceptionV3 architecture utilizes factorized inception blocks, facilitating the interface to pick appropriate kernel-sizes for the convo-lution layers.…”

Section: Discussionmentioning

confidence: 99%

Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images

Ahmad

Khan

Javed

2021

Computers in Biology and Medicine

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Novel Coronavirus is deadly for humans and animals. The ease of its disper-sion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radi-ologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community.

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“…Wang and Wong [28] adopted a convolutional neural network method for the classification of X-Ray images which achieved successfully 83.5% accuracy. A very famous transfer learning model “inception” was used by Wang et al [29] to predict COVID. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet [30] .…”

Section: Discussionmentioning

confidence: 99%

Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification

Verma

Tripathi

Pant

2021

Materials Today: Proceedings

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COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%.

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Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model

Cited by 193 publications

References 23 publications

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images

Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification

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