Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs

2021 ◽  
Author(s):  
Jayalakshmi Mangalagiri ◽  
Jones Sam Sugumar ◽  
Sumeet Menon ◽  
David Chapman ◽  
Yaacov Yesha ◽  
...  
Keyword(s):  
Deep Learning ◽  
Texture Features ◽  
Ct Scans

scholarly journals Classification of Positive COVID-19 CT Scans using Deep Learning

2021 ◽  
Vol 66 (3) ◽  
pp. 2923-2938
Author(s):  
Muhammad Attique Khan ◽  
Nazar Hussain ◽  
Abdul Majid ◽  
Majed Alhaisoni ◽  
Syed Ahmad Chan Bukhari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scans

scholarly journals Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

2020 ◽  
Author(s):  
Giovanni Da Silva ◽  
Aristófanes Silva ◽  
Anselmo De Paiva ◽  
Marcelo Gattass
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Survival Rates ◽  
Texture Features ◽  
Image Database ◽  
Lung Nodules ◽  
Learning Technique

Lung cancer presents the highest mortality rate, besides being one of the smallest survival rates after diagnosis. Thereby, early detection is extremely important for the diagnosis and treatment. This paper proposes three different architectures of Convolutional Neural Network (CNN), which is a deep learning technique, for classification of malignancy of lung nodules without computing the morphology and texture features. The methodology was tested onto the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best accuracy of 82.3%, sensitivity of 79.4% and specificity 83.8%.

scholarly journals Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning

2021 ◽  
Author(s):  
Fakrul Islam Tushar ◽  
Vincent M. D'Anniballe ◽  
Rui Hou ◽  
Maciej A. Mazurowski ◽  
Wanyi Fu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Weakly Supervised ◽  
Body Ct

Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation)

2020 ◽  
Author(s):  
Rohan Abraham ◽  
Ian Janzen ◽  
Saeed Seyyedi ◽  
Sukhinder Khattra ◽  
John Mayo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ct Scans ◽  
Lung Nodules ◽  
Learning Approaches

scholarly journals A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification

2020 ◽  
Vol 11 (2) ◽  
pp. 44-57
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Texture Features ◽  
Classification Performance ◽  
Learning Approaches ◽  
Learning Framework ◽  
Classification Framework ◽  
Feature Based

The classification of plants is one of the most important aims for botanists since plants have a significant part in the natural life cycle. In this work, a leaf-based automatic plant classification framework is investigated. The aim is to compare two different deep learning approaches named Deep Neural Network (DNN) and deep Convolutional Neural Network (CNN). In the case of deep neural network, hybrid shapes and texture features are utilized as hand-crafted features while in the case of the convolution non-handcraft, features are applied for classification. The offered frameworks are evaluated with a public leaf database. From the simulation results, it is confirmed that the deep CNN-based deep learning framework demonstrates superior classification performance than the handcraft feature based approach.

scholarly journals COVID-19 Diagnosis from chest CT Scans: A Weakly Supervised CNN-LSTM Approach

2021 ◽  
Author(s):  
Mustafa Kara ◽  
Zeynep Öztürk ◽  
Sergin Akpek ◽  
Ayşegül Turupcu
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
High Sensitivity ◽  
High Specificity ◽  
Chest Ct ◽  
Ct Scans ◽  
Imaging Data ◽  
Framework Design ◽  
Weakly Supervised

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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