scholarly journals Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mosleh Hmoud Al-Adhaileh ◽  
Ebrahim Mohammed Senan ◽  
Waselallah Alsaade ◽  
Theyazn H. H Aldhyani ◽  
Nizar Alsharif ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Gastrointestinal Diseases ◽  
Activation Function ◽  
Learning Networks ◽  
Video Endoscopy ◽  
Deep Feature ◽  
Short Period

Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

A Similarity-Based Object Classification Using Deep Neural Networks

2019 ◽  
pp. 197-219
Author(s):  
Parvathi R. ◽  
Pattabiraman V.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Random Projection ◽  
Learning Networks ◽  
Learning Models ◽  
External Conditions ◽  
And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

scholarly journals Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with Stacked Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 1732
Author(s):  
Hadis Madani ◽  
Kenneth McIsaac
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Vector ◽  
Remote Sensing Data ◽  
Distance Transform ◽  
Hyperspectral Image Classification ◽  
Spatial Feature ◽  
Learning Techniques

Pixel-wise classification of hyperspectral images (HSIs) from remote sensing data is a common approach for extracting information about scenes. In recent years, approaches based on deep learning techniques have gained wide applicability. An HSI dataset can be viewed either as a collection of images, each one captured at a different wavelength, or as a collection of spectra, each one associated with a specific point (pixel). Enhanced classification accuracy is enabled if the spectral and spatial information are combined in the input vector. This allows simultaneous classification according to spectral type but also according to geometric relationships. In this study, we proposed a novel spatial feature vector which improves accuracies in pixel-wise classification. Our proposed feature vector is based on the distance transform of the pixels with respect to the dominant edges in the input HSI. In other words, we allow the location of pixels within geometric subdivisions of the dataset to modify the contribution of each pixel to the spatial feature vector. Moreover, we used the extended multi attribute profile (EMAP) features to add more geometric features to the proposed spatial feature vector. We have performed experiments with three hyperspectral datasets. In addition to the Salinas and University of Pavia datasets, which are commonly used in HSI research, we include samples from our Surrey BC dataset. Our proposed method results compares favorably to traditional algorithms as well as to some recently published deep learning-based algorithms.

scholarly journals Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

2021 ◽  
Vol 7 ◽  
pp. e773
Author(s):  
Jan Egger ◽  
Antonio Pepe ◽  
Christina Gsaxner ◽  
Yuan Jin ◽  
Jianning Li ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Research Impact ◽  
Basic Structure ◽  
Search Term ◽  
Learning Networks ◽  
Scientific Disciplines ◽  
Short Period

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term ‘deep learning’, and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.

scholarly journals Covid-19 Detection & Classification of chest X-rays using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2614-2619
Author(s):  
Aditya Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Neural Network Model ◽  
X Rays ◽  
X Ray ◽  
Short Period ◽  
Medical Researchers ◽  
Entire World ◽  
Difficult Times

Abstract: The deadly Covid-19 virus, also known as the Coronavirus has affected the entire world in a short period of time. This pandemic has affected a lot of people in the entire world and caused many deaths. In these difficult times, it is important for the doctors and the medical researchers to differentiate accurately between positive cases and negative cases. This CNN (Convolutional Neural Network) model will allow us to classify X-ray images into positive cases and the normal ones. This dataset is collected from different public sources as well as from some hospitals and physicians. Our goal is to take help from these X- ray images and develop a model where it predicts and classifies the infected cases. Keywords: CNN, Prediction, Classification, Features, Training, Testing, Deep Learning

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