Classification of COVID19 X-ray Images Based on Transfer Learning InceptionV3 Deep Learning Model

2021 ◽  
pp. 111-119
Author(s):  
Hassan Amin ◽  
Ashraf Darwish ◽  
Aboul Ella Hassanien
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Model ◽  
X Ray ◽  
Deep Learning Model

Classification of Pulmonary Viruses X-ray and Detection of COVID-19 Based on Invariant of Inception-V 3 Deep Learning Model

2021 ◽  
Author(s):  
Zubair Saeed ◽  
Misha Urooj Khan ◽  
Ali Raza ◽  
Hareem Khan ◽  
Javaria Javed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
X Ray ◽  
Deep Learning Model

scholarly journals Classification of Knee Osteoarthritis Severity Using Modified Masks to Preprocess X-ray Images in a Deep Learning Model

2021 ◽  
Author(s):  
Ching-Chung Yang
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Knee Osteoarthritis ◽  
Object Detection ◽  
Image Classification ◽  
Learning Model ◽  
X Ray ◽  
Medical Image Classification ◽  
Deep Learning Model

We propose a concise approach to facilitate the deep learning model for medical image classification of knee osteoarthritis severity. The characteristics of the input X-ray images are sharpened by a modified 5×5 mask before training and testing in this work. We compare the inference accuracies of two experiments using the same architecture with images sharpened and not sharpened respectively. And we find it tangible that the former performs much better than the latter. This technique could also be helpful when applied onto the edge devices for object detection and image segmentation.

scholarly journals A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Omar Faruk ◽  
Eshan Ahmed ◽  
Sakil Ahmed ◽  
Anika Tabassum ◽  
Tahia Tazin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Computer Assisted ◽  
Promising Technique ◽  
Suggested Approach ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal 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.

Detection and classification of arrhythmia using an explainable deep learning model

2021 ◽  
Author(s):  
Yong-Yeon Jo ◽  
Joon-myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Yong-Hyeon Cho ◽  
Jae-Hyun Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Deep Learning Model
Sign in / Sign up

Export Citation Format

Share Document