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.

Medical Image Classification based on an Adaptive Size Deep Learning Model

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-18
Author(s):  
Xiangbin Liu ◽  
Jiesheng He ◽  
Liping Song ◽  
Shuai Liu ◽  
Gautam Srivastava
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Rapid Development ◽  
Learning Model ◽  
Fine Tuning ◽  
Optimal Size ◽  
Image Dataset ◽  
Medical Image Classification ◽  
Deep Learning Model

With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.

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

Classification of histopathological images of breast cancer using an improved convolutional neural network model

2021 ◽  
pp. 1-12
Author(s):  
Yunfeng Yang ◽  
Chen Guan
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Image Classification ◽  
Network Model ◽  
Learning Model ◽  
Image Features ◽  
Data Set ◽  
Study Results ◽  
Deep Learning Model

The accurately automatic classification of medical pathological images has always been an important problem in the field of deep learning. However, the traditional manual extraction of features and image classification usually requires in-depth knowledge and more professional researchers to extract and calculate high-quality image features. This kind of operation generally takes a lot of time and the classification effect is not ideal. In order to solve these problems, this study proposes and tests an improved network model DenseNet-201-MSD to accomplish the task of classification of medical pathological images of breast cancer. First, the image is preprocessed, and the traditional pooling layer is replaced by multiple scaling decomposition to prevent overfitting due to the large dimension of the image data set. Second, the BN algorithm is added before the activation function Softmax and Adam is used in the optimizer to optimize performance of the network model and improve image recognition accuracy of the network model. By verifying the performance of the model using the BreakHis dataset, the new deep learning model yields image classification accuracy of 99.4%, 98.8%, 98.2%and 99.4%when applying to four different magnifications of pathological images, respectively. The study results demonstrate that this new classification method and deep learning model can effectively improve accuracy of pathological image classification, which indicates its potential value in future clinical application.

scholarly journals Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 58006-58017 ◽  
Author(s):  
R. Joshua Samuel Raj ◽  
S. Jeya Shobana ◽  
Irina Valeryevna Pustokhina ◽  
Denis Alexandrovich Pustokhin ◽  
Deepak Gupta ◽  
...  
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Learning Model ◽  
Optimal Feature Selection ◽  
Medical Image Classification ◽  
Internet Of Medical Things ◽  
Deep Learning Model ◽  
Optimal Feature

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.

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