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Deep Learning Based DR Medical image Classification 6th

Deep Learning Based DR Medical Image Classification

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6th international conference on computer vision and image processing, cvip 2021

作者： Deshmukh, Preeti Gaikwad, Arun N. Vidya Vikas Pratisthan Institute of Engineering and Technology Maharashtra Solapur India Zeal College of Engineering and Research Center Maharashtra Pune India

ISBN: (纸本)9783031113482

Diabetic retinopathy (DR) is a prevailing disease that causes blindness among diabetic patients. The timely intervention with the regular fundus photography screening is the efficient way to cope with this disease. Screening of a large number of diabetic patients urges to the computer-aided and fully automatic DR diagnosis. Deep neural networks are gaining more attention due to their effectiveness in various tasks. The diagnosis of the can be made automatic and accurate suggestions can be provided to DR patients. The classification of DR images is challenging and important step. Therefore, in this paper, we have proposed a learning-based DR image reconstruction followed by a classification approach for DR image classification. Initially, the learning-based image reconstruction approach is proposed with a multi-encoder decoder network and residual inception block to delve into the features of input medical image and convert it into a set of abstract features followed by the reconstruction of the input medical image. The features from encoder are the abstract version of actual image representation which can be used for robust reconstruction for the input image. Therefore, these encoded features are used for DR image classification. The results analysis of the proposed framework with existing SOTA feature extraction algorithms is conducted on the MESSIDOR database for DR image classification. From the results’ analysis, it is evident that the proposed reconstruction-based classification framework outperforms the existing SOTA feature extraction algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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An Hour-Glass CNN for Language Identification of Indic Texts in Digital images 1

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6th international conference on computer vision and image processing, cvip 2021

作者： Chakraborty, Neelotpal Mollah, Ayatullah Faruk Basu, Subhadip Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering Aliah University Kolkata700160 India

ISBN: (数字)9783031113499

ISBN: (纸本)9783031113482

Understanding multi-lingual texts in any digital image calls for identifying the corresponding languages of the localized texts. India houses a multi-lingual ambience which necessitates the pursuit of an efficient model that is robust against various complexities and successfully identifies the language of Indic texts. This paper presents a deep learning based Convolutional Neural Network (CNN) model having an hour-glass like structure, for classifying texts in popular Indic languages like Bangla, English and Hindi. A new dataset, called Indic Texts in Digital images (ITDI), is also presented which is a collection of text images, both scene and born-digital, written in Bangla, English and Hindi. The performance of the hour-glass CNN is evaluated upon standard Indic dataset like AUTNT giving an accuracy of 90.93% which is higher than most state-of-the-art models. The proposed model is also used to benchmark the performance on ITDI dataset with a reasonable accuracy of 85.18%. Sample instances of the proposed ITDI dataset can be found at: https://***/NCJUCSE/ITDI © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Glass

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Attention Residual Capsule Network for Dermoscopy image Classification 1

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7th international conference on computer vision and image processing, cvip 2022

作者： Pal, Anabik Ray, Sounak Antani, Sameer Garain, Utpal Department of Computer Science and Engineering SRM University AP Amaravati India National Library of Medicine National Institutes of Health MD United States CVPR Unit Indian Statistical Institute Kolkata India Department of Computer Science Columbia University New York City United States Centre for AIML Indian Statistical Institute Kolkata India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Automated analysis of dermoscopic images for detecting malignant lesions can improve diagnostic performance and reduce premature deaths. While several automated classification algorithms using deep convolutional neural network (DCNN) models have been proposed, the need for performance improvement remains. The key limitations of developing a robust DCNN model for the dermoscopic image classification are (a) sub-sampling or pooling layer in traditional DCNN has theoretical drawbacks in capturing object-part relationship, (b) increasing the network depth can improve the performance but is prone to suffer from the vanishing gradient problem, and (c) due to imbalanced dataset, the trained DCNN tends to be biased towards the majority classes. To overcome these limitations, we propose a novel deep Attention Residual Capsule Network (ARCN) for dermoscopic image classification to diagnose skin diseases. The proposed model combines the concept of residual learning, self-attention mechanism, and capsule network. The residual learning is employed to address the vanishing gradient problem, the self-attention mechanism is employed to prioritize important features without using any extra learnable parameters, capsule network is employed to cope up with information loss due to the sub-sampling (max-pooling) layer. To deal with the classifier’s bias toward the majority classes, a novel Mini-Batch-wise weight-balancing Focal Loss strategy is proposed. HAM10000, a benchmark dataset of dermoscopic images is used to train the deep model and evaluate the performance. The ARCN-18 (modifica-tion of ResNet-18) network trained with the proposed loss produces an accuracy of 0.8206 for the considered test set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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Classroom Slide Narration System 1

引用

6th international conference on computer vision and image processing, cvip 2021

作者： Jobin, K.V. Mondal, Ajoy Jawahar, C.V. IIIT-Hyderabad Hyderabad India

ISBN: (数字)9783031113499

ISBN: (纸本)9783031113482

Slide presentations are an effective and efficient tool used by the teaching community for classroom communication. However, this teaching model can be challenging for the blind and visually impaired (vi) students. As such student required a personal human assistance for understand the presented slide. This shortcoming motivates us to design a Classroom Slide Narration System (csns) that generates audio descriptions corresponding to the slide content. This problem poses as an image-to-markup language generation task. The initial step is to extract logical regions such as title, text, equation, figure, and table from the slide image. In the classroom slide images, the logical regions are distributed based on the location of the image. To utilize the location of the logical regions for slide image segmentation, we propose the architecture, Classroom Slide Segmentation Network (cssn). The unique attributes of this architecture differs from most other semantic segmentation networks. Publicly available benchmark datasets such as WiSe and SPaSe are used to validate the performance of our segmentation architecture. We obtained 9.54% segmentation accuracy improvement in WiSe dataset. We extract content (information) from the slide using four well-established modules such as optical character recognition (ocr), figure classification, equation description, and table structure recognizer. With this information, we build a Classroom Slide Narration System (csns) to help vi students understand the slide content. The users have given better feedback on the quality output of the proposed csns in comparison to existing systems like Facebook’s Automatic Alt-Text (aat) and Tesseract. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image segmentation

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Complex Texture Features Learned by Applying Randomized Neural Network on Graphs 11

Complex Texture Features Learned by Applying Randomized Neur...

引用

11th international conference on image processing Theory, Tools and Applications (IPTA)

作者： Zielinski, Kallil M. C. Ribas, Lucas C. Scabini, Leonardo F. S. Bruno, Odemir M. Univ Sao Paulo Sao Carlos Inst Phys Sao Carlos SP Brazil

ISBN: (纸本)9781665469647

Since the 1960s, texture has become one of the moststudied visual attribute of images for analysis and classification tasks. Among many different approaches such as statistical, spectral, structural and model-based, there are also methods that rely on analyzing the image complexity and learning techniques. These recent approaches are receiving attention for its promising results in the past few years. This paper proposes a method that combines complex networks and randomized neural networks. In the proposed approach, the texture image is modeled as a complex network, and the information measures obtained from the topological properties of the network are then used to train the RNN in order to learn a representation of the modeled image. Our proposal has proven to perform well in comparison to other literature approaches in two different texture databases. Our method also achieved a high performance in a very challenging biological problem of plant species recognition. Thus, the method is a promising option for different tasks of image analysis.

关键词： Complex Networks computer vision Feature Extraction Texture Analysis Pattern Recognition

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Language Driven image Editing via Transformers 34

Language Driven Image Editing via Transformers

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34th IEEE international conference on Tools with Artificial Intelligence (ICTAI)

作者： Santos, Rodrigo Branco, Antonio Silva, Joao Univ Lisbon Fac Sci NLX Natural Language & Speech Grp Dept InformaFac Ciencias Lisboa Campo Grande P-1749016 Lisbon Portugal

ISBN: (纸本)9798350397444

With the emergence of specifically tailored neural architectures that cope with both modalities, cross-modal language and image processing has attracted increasing attention. A major motivation has been the search for a quantum leap in language understanding supported by visual grounding, which has been oriented mostly to solve tasks where language descriptions of images are to be provided, and vice-versa, where images are to be generated on the basis of keywords. Adopting a distinct angle of inquiry, this paper addresses rather the cross-modal challenge of language driven image design, focusing on the task of editing an image on the basis of language instructions to modify it. And adopting as well a distinct research path, which dispenses with specifically tailored architectures, the approach proposed here resorts rather to a general purpose, suitably instantiated neural architecture of the Transformer class. Experimentation with this approach delivered very encouraging results, empirically demonstrating that this is an effective methodology for language driven image design and the basis for further advances in cross-modal processing and its applications with affordable compute and data.

关键词： Language Driven image Design Conditional image Alteration computer vision Natural Language processing

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A Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images 6th

A Deep Learning Framework for the Classification of Lung ...

引用

6th international conference on computer vision and image processing, cvip 2021

作者： Vyshnavi, M. Abraham, Bejoy Ramachandran, Sivakumar Department of Electronics and Communication Engineering College of Engineering Trivandrum Kerala Thiruvananthapuram India Department of Computer Science and Engineering College of Engineering Perumon Kerala Kollam India

ISBN: (纸本)9783031113451

Automated screening and classification of various lesions in medical images can assist clinicians in the treatment and management of many systemic and localized diseases. Manual inspection of medical images is often expensive and time-consuming. Automatic image-analysis employing computers can alleviate the difficulties of manual methods for screening a large amount of generated images. Inspired by the great success of deep learning, we propose a diagnostic system that can classify various lung diseases from chest X-ray images. In this work, chest X-ray images are applied to a deep-learning algorithm for classifying images into pneumothorax, viral pneumonia, COVID-19 pneumonia and healthy cases. The proposed system is trained with a set of 4731 chest X-ray images, and obtained an overall classification accuracy of 99% in images taken from two publicly available data sets. The promising results demonstrate the proposed system’s effectiveness as a diagnostic tool to assist health care professionals for categorizing images in any of the four classes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Fire Detection Model Using Deep Learning Techniques 6th

Fire Detection Model Using Deep Learning Techniques

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6th international conference on computer vision and image processing, cvip 2021

作者： Pundir, Arun Singh Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9783031113482

Frame-based and motion-based deep learning framework have been employed in this paper for fire detection. This paper presents a novel method for fire detection that employs different Convolutional Neural Networks (CNN) architectures for fire detection. Firstly, frame based features such as fire-color, fire texture, and analysis of perimeter disorder which are present in the still images were extracted using transfer learning. Secondly, motion-based CNN is used for extracting motion based features of fire such as growing region, moving area and uprising part detection. Optical flow was employed to calculate the motion of frame intensities. These extracted intensity features which being projected as image were fed into Deep CNN for find out the uniqueness in the motion of fire. Features from both the models are combined to feed into different classifiers. Method is tested on different varieties of non-fire videos which are similar to forest fire such as fire-works, sun based videos, traffic at night and flower-valley videos. Accuracy on such similar situations has proven the precision and robustness in the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Research on SAR image processing Performance Based on OpenMP and CUDA Parallel Model

Research on SAR Image Processing Performance Based on OpenMP...

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2022 international conference on computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Zhao, Yanbin Liu, Yang Innovation Academy for Microsatellites Chinese Academy of Sciences Hefei China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

In the field of remote sensing image processing, Synthetic Aperture Radar(SAR) image has a large amount of data and takes a long time to process. Especially in the military field and disaster detection, conventional serial processing methods cannot meet the requirements of real-time performance. To solve this problem, the high-performance computing advantages of parallel processing are utilized. The optimization effect of SAR image processing performance was studied based on OpenMP shared memory parallel processing method and CUDA parallel model. Combined with the use of OpenCV computer vision library, basic image processing tasks and parallel processing framework were designed. According to the comparison of the execution time between parallel processing and serial processing, the SAR image preprocessing speed is increased by 2 to 16 times. It is concluded that the parallel computing model is an effective fast SAR image processing optimization scheme. © 2022 IEEE.

关键词： Synthetic aperture radar

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Application of Deep Learning Techniques for Prostate Cancer Grading Using Histopathological images 1

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6th international conference on computer vision and image processing, cvip 2021

作者： Gour, Mahesh Jain, Sweta Shankar, Uma Maulana Azad National Institute of Technology Bhopal462003 India

ISBN: (数字)9783031113468

ISBN: (纸本)9783031113451

Prostate cancer is one of the most dangerous cancers that affect men around the world. Pathologists use a variety of approaches to grade prostate cancer. Among them, microscopic examination of biopsy tissue images is the most efficient method. A timely and accurate diagnosis plays a critical role in preventing cancer from progressing. The recent achievement of deep learning (DL), notably in the convolution neural networks (CNN), is exceptional in medicine. In this study, we have investigated and compared the performance of the state-of-the-art CNN models, namely MobileNet-V2, ResNet50, DenseNet121, DenseNet169, VGG16, VGG19, Xception, InceptionV3, InceptionResNet-V2, and EfficientNet-B7 for prostate cancer grading using histopathological images. The performance of pre-trained CNNs has been evaluated on the publicly available Prostate cancer grade assessment (PANDA) dataset. On this multi-class classification problem, the EfficientNet-B7 model has achieved the highest classification accuracy of 90.90%. With such a high rate of success, the EfficientNet-B7 model may be a useful method for pathologists in determining the stage of prostate cancer. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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