scholarly journals Application of the transfer learning to the medical images texture classification task

2020 ◽  
Vol 224 ◽  
pp. 01020
Author(s):  
M Privalov ◽  
M Stupina
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Medical Images ◽  
Texture Classification ◽  
Classification Task ◽  
Radiotherapy Planning ◽  
Digital Radiograph ◽  
Magnetic Resonance Imaging Mri ◽  
Images Processing ◽  
Brachy Therapy

This study is conducted to determine effectiveness and perspectives of application of the transfer learning approach to the medical images classification task. There are a lot of medical studies that involve image acquisition, such as XRay radiography, ultrasonic scanning, computer tomography (CT), magnetic resonance imaging (MRI) etc. Besides those medical procedures there are different operations that use medical images processing including but not limited to digital radiograph reconstruction (DRR), radiotherapy planning, brachy therapy planning. All those tasks could be effectively performed with help of software capable to perform segmentation, classification and object recognition. Those capabilities are naturally depend on neural classifiers. Presented work investigates different approaches to solving image classification task with neural networks, specifically, using pre-processing for feature extraction and end-to-end application of convolutional neural networks (CNN). Due to requirement of significantly big datasets and large computing power CNNs sometimes may appear difficult to train, so our results pay attention to application of transfer learning technique that can potentially relax requirements to classifier training. The conclusions of this study state that transfer learning can be effectively used for classification tasks, especially texture classification.

scholarly journals DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74901-74913 ◽  
Author(s):  
Asmaa Abbas ◽  
Mohammed M. Abdelsamea ◽  
Mohamed Medhat Gaber
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Medical Images

scholarly journals Deep Covid - Coronavirus Diagnosis Using Deep Neural Networks and Transfer Learning

2021 ◽  
Author(s):  
Abhinav Sagar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Classification Task ◽  
X Ray ◽  
Deadly Disease ◽  
The Right

Coronavirus pandemic is a deadly disease and is a global emergency. If not acted upon by drugs at the right time, coronavirus may result in the death of individuals. Hence early diagnosis is very important along the progress of the disease. This paper focuses on coronavirus detection using x-ray images, for automating the diagnosis pipeline using convolutional neural networks and transfer learning. This could be deployed in places where radiologists are not easily available in order to detect the disease at very early stages. In this study we propose our deep learning architecture for the classification task, which is trained with modified images, through multiple steps of preprocessing. Our classification method uses convolutional neural networks and transfer learning architecture for classifying the images. Our findings yield an accuracy value of 91.03%, precision of 89.76 %, recall value of 96.67% and F1 score of 93.09%.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

A Novel Secure and Robust encryption scheme for medical images

2020 ◽  
Vol 16 ◽  
Author(s):  
Siyamol Chirakkarottu ◽  
Sheena Mathew
Keyword(s):  
Medical Images ◽  
Technological Development ◽  
Imaging Techniques ◽  
Chaotic Map ◽  
Two Phase ◽  
Positron Emission ◽  
Medical Diagnostic ◽  
Wireless Medium ◽  
Health Care Applications ◽  
Magnetic Resonance Imaging Mri

Background: Medical imaging encloses different imaging techniques and processes to image the human body for medical diagnostic and treatment purposes. Hence it plays an important role to improve public health. The technological development in biomedical imaging specifically in X-ray, Computed Tomography (CT), nuclear ultrasound including Positron Emission Tomography (PET), optical and Magnetic Resonance Imaging (MRI) can provide valuable information unique to a person. Objective: In health care applications, the images are needed to be exchanged mostly over wireless medium. The diagnostic images with confidential information of a patient need to be protected from unauthorized access during transmission. In this paper, a novel encryption method is proposed to improve the security and integrity of medical images. Methods: Chaotic map along with DNA cryptography is used for encryption. The proposed method describes a two phase encryption of medical images. Results: Performance of the proposed method is also tested by various analysis metrics. Robustness of the method against different noises and attacks is analyzed. Conclusion: The results show that the method is efficient and well suitable to medical images.

scholarly journals Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Gustaf Halvardsson ◽  
Johanna Peterson ◽  
César Soto-Valero ◽  
Benoit Baudry
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Web Application ◽  
Training Dataset ◽  
Motion Processing ◽  
Image Perception ◽  
Sign Languages ◽  
High Level

AbstractThe automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

scholarly journals Improving Semi-Supervised Learning for Audio Classification with FixMatch

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1807
Author(s):  
Sascha Grollmisch ◽  
Estefanía Cano
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Data Transfer ◽  
State Of The Art ◽  
Training Data ◽  
Audio Classification ◽  
Image Domain ◽  
Full Dataset ◽  
Audio Data

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

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