Performance Evaluation of Classical Classifiers and Deep Learning Approaches for Polymers Classification Based on Hyperspectral Images

The deep learning approaches have drawn much focus of the researchers in the area of object recognition because of their implicit strength of conquering the shortcomings of classical approaches dependent on hand crafted features. In the last few years, the deep learning techniques have been made many developments in object recognition. This paper indicates some recent and efficient deep learning frameworks for object recognition. The up to date study on recently developed a deep neural network based object recognition methods is presented. The various benchmark datasets that are used for performance evaluation are also discussed. The applications of the object recognition approach for specific types of objects (like faces, buildings, plants etc.) are also highlighted. We conclude up with the merits and demerits of existing methods and future scope in this area.

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Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Real-Time Image Processing and Deep Learning 2019 ◽

10.1117/12.2519098 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yaroub Elloumi ◽

Mohamed Akil ◽

Henda Boudegga

Keyword(s):

Performance Evaluation ◽

Deep Learning ◽

Fundus Images ◽

Learning Approaches ◽

Ocular Diseases ◽

And Performance

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

SSRN Electronic Journal ◽

10.2139/ssrn.3411719 ◽

2019 ◽

Author(s):

Qian Wu ◽

Weiling Zhao ◽

Xiaobo Yang ◽

Hua Tan ◽

Lei You ◽

...

Keyword(s):

Risk Factors ◽

Deep Learning ◽

Learning Approaches

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The Problem of Fraudulent Content on the Web: Deep Learning Approaches

SSRN Electronic Journal ◽

10.2139/ssrn.3575411 ◽

2020 ◽

Author(s):

Priyanka Meel ◽

Farhin Bano ◽

Dr. Dinesh K. Vishwakarma

Keyword(s):

Deep Learning ◽

Learning Approaches ◽

The Web

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy

Scientific Reports ◽

10.1038/s41598-021-84510-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shan Guleria ◽

Tilak U. Shah ◽

J. Vincent Pulido ◽

Matthew Fasullo ◽

Lubaina Ehsan ◽

...

Keyword(s):

Deep Learning ◽

Diagnostic Accuracy ◽

High Sensitivity ◽

Confocal Laser Endomicroscopy ◽

Confocal Laser ◽

Learning Approaches ◽

Learning Models ◽

Whole Slide Image ◽

Slide Image ◽

Level Model

AbstractProbe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches—a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

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