A Comparison of the Deep Learning Methods for Solving Seafloor Image Classification Task

2017 ◽  
pp. 442-453 ◽  
Author(s):  
Tadas Rimavicius ◽  
Adas Gelzinis
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Classification Task ◽  
Learning Methods

scholarly journals Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kinshuk Sengupta ◽  
Praveen Ranjan Srivastava
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Machine Learning Algorithms ◽  
Classification Task ◽  
Clinical Image ◽  
Prototype Model ◽  
Learning Models ◽  
Accuracy Measure ◽  
Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

Mapping Fluvial Landforms with Deep Similarity Learning

2021 ◽  
Author(s):  
Patrice Carbonneau
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Color Image ◽  
A Priori ◽  
Similarity Learning ◽  
Dimension Vector ◽  
Learning Methods ◽  
River Meandering ◽  
Sentinel 2 ◽  
Training Sets

<p>Semantic image classification as practised in Earth Observation is poorly suited to mapping fluvial landforms which are often composed of multiple landcover types such as water, riparian vegetation and exposed sediment. Deep learning methods developed in the field of computer vision for the purpose of image classification (ie the attribution of a single label to an image such as cat/dog/etc) are in fact more suited to such landform mapping tasks. Notably, Convolutional Neural Networks (CNN) have excelled at the task of labelling images. However, CNN are notorious for requiring very large training sets that are laborious and costly to assemble. Similarity learning is a sub-field of deep learning and is better known for one-shot and few-shot learning methods. These approaches aim to reduce the need for large training sets by using CNN architectures to compare a single, or few, known examples of an instance to a new image and determining if the new image is similar to the provided examples. Similarity learning rests on the concept of image embeddings which are condensed higher-dimension vector representations of an image generated by a CNN. Ideally, and if a CNN is suitably trained, image embeddings will form clusters according to image classes, even if some of these classes were never used in the initial CNN training.</p><p> </p><p>In this paper, we use similarity learning for the purpose of fluvial landform mapping from Sentinel-2 imagery. We use the True Color Image product with a spatial resolution of 10 meters and begin by manually extracting tiles of 128x128 pixels for 4 classes: non-river, meandering reaches, anastomosing reaches and braiding reaches. We use the DenseNet121 CNN topped with a densely connected layer of 8 nodes which will produce embeddings as 8-dimension vectors. We then train this network with only 3 classes (non-river, meandering and anastomosing) using a categorical cross-entropy loss function. Our first result is that when applied to our image tiles, the embeddings produced by the trained CNN deliver 4 clusters. Despite not being used in the network training, the braiding river reach tiles have produced embeddings that form a distinct cluster. We then use this CNN to perform few-shot learning with a Siamese triplet architecture that will classify a new tile based on only 3 examples of each class. Here we find that tiles from the non-river, meandering and anastomising class were classified with F1 scores of 72%, 87% and 84%, respectively. The braiding river tiles were classified to an F1 score of 80%. Whilst these performances are lesser than the 90%+ performances expected from conventional CNN, the prediction of a new class of objects (braiding reaches) with only 3 samples to 80% F1 is unprecedented in river remote sensing. We will conclude the paper by extending the method to mapping fluvial landforms on entire Sentinel-2 tiles and we will show how we can use advanced cluster analyses of image embeddings to identify landform classes in an image without making a priori decisions on the classes that are present in the image.</p>

Autonomous Martian rock image classification based on transfer deep learning methods

2020 ◽  
Vol 13 (3) ◽  
pp. 951-963 ◽  
Author(s):  
Jialun Li ◽  
Li Zhang ◽  
Zhongchen Wu ◽  
Zongcheng Ling ◽  
Xueqiang Cao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Learning Methods ◽  
Rock Image

scholarly journals HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides

2021 ◽  
Author(s):  
Qinze Yu ◽  
Zhihang Dong ◽  
Xingyu Fan ◽  
Licheng Zong ◽  
Yu Li
Keyword(s):  
Deep Learning ◽  
Antimicrobial Peptides ◽  
Binary Classification ◽  
Classification Task ◽  
Protein Sequence Database ◽  
Learning Methods ◽  
Small Perturbations ◽  
Label Protein ◽  
Deep Forest ◽  
Wet Lab

Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immuneresponse and combating antibiotic resistance, and more broadly, precision medicine and public health. Therehave been extensive studies on the statistical and computational approaches to identify (i) whether a peptide is anantimicrobial peptide (AMP) or a non-AMP and (ii) which targets are these sequences effective to (Gram-positive,Gram-negative, etc.). Despite the existing deep learning methods on this problem, most of them are unable tohandle the small AMP classes (anti-insect, anti-parasite, etc.). And more importantly, some AMPs can havemultiple targets, which the previous methods fail to consider. In this study, we build a diverse and comprehensivemulti-label protein sequence database by collecting and cleaning amino acids from various AMP databases.To generate efficient representations and features for the small classes dataset, we take advantage of a proteinlanguage model trained on 250 million protein sequences. Based on that, we develop an end-to-end hierarchicalmulti-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, itfurther predicts what targets the AMP can effectively kill from eleven available classes. Extensive experimentssuggest that our framework outperforms state-of-the-art models in both the binary classification task and themulti-label classification task, especially on the minor classes. Compared with the previous deep learning methods,our method improves the performance on macro-AUROC by 11%. The model is robust against reduced featuresand small perturbations and produces promising results. We believe HMD-AMP contribute to both the future wet-lab investigations of the innate structural properties of different antimicrobial peptides and build promising empirical underpinnings for precise medicine with antibiotics.

scholarly journals NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS

2018 ◽  
Vol 17 (2) ◽  
Author(s):  
Sudipta Singha Roy ◽  
Mahtab Ahmed ◽  
Muhammad Aminul Haque Akhand
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Noisy Image ◽  
Learning Methods

scholarly journals Deep Learning for Video Classification: A Review

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Classification Task ◽  
Future Research ◽  
Video Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Research Directions ◽  
Concise Review ◽  
State Of Art

<div><div><div><p>Video classification task has gained a significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition to the importance of video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are a number of existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers are either outdated, and therefore, do not include the recent state-of-art works or they have some limitations. In order to provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the data sets used. To make the review self- contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided, and the critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.</p></div></div></div>

scholarly journals Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

2019 ◽  
Vol 113 ◽  
pp. 47-54 ◽  
Author(s):  
Titus J. Brinker ◽  
Achim Hekler ◽  
Alexander H. Enk ◽  
Joachim Klode ◽  
Axel Hauschild ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Classification Task
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