scholarly journals A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks

2021 ◽  
Vol 18 (5) ◽  
pp. 5029-5046
Author(s):  
Meteb M. Altaf ◽  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Learning Model ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks ◽  
Deep Learning Model

scholarly journals Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos

2021 ◽  
Vol 10 (3) ◽  
pp. 137
Author(s):  
Youngok Kang ◽  
Nahye Cho ◽  
Jiyoung Yoon ◽  
Soyeon Park ◽  
Jiyeon Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Field Studies ◽  
Learning Model ◽  
Learning Technologies ◽  
Regional Study ◽  
Processing Technologies ◽  
Final Model ◽  
Urban Image ◽  
Deep Learning Model

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.

Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning

2021 ◽  
Vol 27 ◽  
Author(s):  
Qi Zhou ◽  
Wenjie Zhu ◽  
Fuchen Li ◽  
Mingqing Yuan ◽  
Linfeng Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Intracranial Hemorrhage ◽  
Intraventricular Hemorrhage ◽  
Learning Model ◽  
Normal Group ◽  
Subdural Hemorrhage ◽  
Model Assessment ◽  
Learning Models ◽  
Deep Learning Model

Objective: To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage. Method: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. Results: The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9. Conclusion: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

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