Classification of Pneumonia Cell Images Using Improved ResNet50 Model

Çınar, Ahmet Cevahir; Yıldırım, Muhammed; Eroğlu, Yeşim

doi:10.18280/ts.380117

Cited by 41 publications

(19 citation statements)

References 26 publications

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“…Deep CNN networks such as ResNet [ 14 ] and DenseNet [ 15 ] have greatly improved the accuracy of image classification. However, in addition to accuracy, computational complexity is also an important index to be considered by the CNN network.…”

Section: Methodsmentioning

confidence: 99%

An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

Fang

Yang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of deep learning, automatic lesion detection is used widely in clinical screening. To solve the problem that existing deep learning-based cervical precancerous lesion detection algorithms cannot meet high classification accuracy and fast running speed at the same time, a ShuffleNet-based cervical precancerous lesion classification method is proposed. By adding channel attention to the ShuffleNet, the network performance is improved. In this study, the image dataset is classified into five categories: normal, cervical cancer, LSIL (CIN1), HSIL (CIN2/CIN3), and cervical neoplasm. The colposcopy images are expanded to solve the problems of the lack of colposcopy images and the uneven distribution of images from each category. For the test dataset, the accuracy of the proposed CNN models is 81.23% and 81.38%. Our classifier achieved an AUC score of 0.99. The experimental results show that the colposcopy image classification network based on artificial intelligence has good performance in classification accuracy and model size, and it has high clinical applicability.

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Section: Methodsmentioning

confidence: 99%

An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

Fang

Yang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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“…In this way, all kinds of data can be used effectively in artificial intelligence models. For example, image processing, an active field in its own right, has been fully integrated with deep learning and artificial intelligence [6][7][8].…”

Section: Deep Learning Algorithms and Grad-cammentioning

confidence: 99%

Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

Yi̇ği̇t¹,

Şengöz²,

Özmen³

et al. 2022

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Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. Against this backdrop, this study examines a new and original dataset using the deep learning algorithm, and visualizes the output with gradient-weighted class activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a detailed questionnaire survey was conducted with the pathologists on these images. Both the decision-making processes and the explanations were verified, and the accuracy of the output was tested. The research results greatly help pathologists in the diagnosis of paratuberculosis.

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“…With the pooling layer, computational costs and the input size of the next layer are reduced. The main purpose of this layer is to get better feature maps [18]. The size of the feature map obtained after the pooling layer is calculated by Eqns.…”

Section: Cnn Architectures Layers and Lstm Networkmentioning

confidence: 99%

Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model

Yıldırım¹

2022

RCES

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Technological developments have led to the emergence of different platforms. Social media platforms are one of the most used platforms recently. In this study, a text-based study was conducted on fake news sharing about COVID-19 in online social networks with Shallow Learning (SL) and Deep Learning (DL) methods. In order to classify the news in the dataset, the news in the dataset is converted into a format that can be understood by the machines in the preprocessing step. In the study, the glove method was used for word representation. The document matrix obtained using the glove method was classified with the proposed hybrid model. In the proposed hybrid model, LSTM and CNN structures are used together. In addition, different Shallow Learning methods accepted in the literature were used to compare the performances of the proposed model, and the results were obtained and these results were compared with the proposed model. Among these models, the most successful results were obtained in the proposed hybrid model. When the performance evaluation metrics obtained are examined, it is obvious that the proposed model can be used to solve many other social media and network problems related to COVID-19.

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Classification of Pneumonia Cell Images Using Improved ResNet50 Model

Cited by 41 publications

References 26 publications

An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model

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