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HSADML: Hyper-Sphere Angular Deep Metric Based Learning for Brain Tumor Classification 12th

HSADML: Hyper-Sphere Angular Deep Metric Based Learning for ...

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12th indian conference on computer vision, graphics, and image processing, ICVGIP 2021

作者： Verma, Aman Singh, Vibhav Prakash Department of Electronics and Communications National Institute of Technology Raipur Raipur India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811941351

Brain Tumors are an abnormal mass of clustered cells penetrating regions of the brain. their timely identification and classification help doctors to provide appropriate treatment. However, Classification of Brain Tumors is quite intricate because of high-intra-class similarity and low-inter- class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. this all leads to hampering the generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables Deep Metric Learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embedding to enhance differentiability between the classes. With the utilization of SphereFace loss-based deep metric learning it is ensured that samples from class are clustered together while the different ones are pushed apart. Results reflect the prominence of the approach, the proposed framework achieved state-of-the-art 98.69% validation accuracy using k-NN (k = 1) and this is significantly higher than normal Soft Max Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class reparability and intra-class closeness. Experimental analysis is done over various classifiers and loss function settings suggest potential in the approach. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Brain

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Transfer learning by finetuning pretrained CNNs entirely with synthetic images 6th

Transfer learning by finetuning pretrained CNNs entirely wit...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Rajpura, Param Aggarwal, Alakh Goyal, Manik Gupta, Sanchit Talukdar, Jonti Bojinov, Hristo Hegde, Ravi Indian Institute of Technology Gandhinagar Gandhinagar382355 India Varanasi Varanasi221005 India BITS Hyderabad Hyderabad500078 India Nirma Institute of Technology Ahmedabad382481 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

We show that finetuning pretrained CNNs entirely on synthetic images is an effective strategy to achieve transfer learning. We apply this strategy for detecting packaged food products clustered in refrigerator scenes. A CNN pretrained on the COCO dataset and fine-tuned with our 4000 synthetic images achieves mean average precision (mAP @ 0.5-IOU) of 52.59 on a test set of real images (150 distinct products as objects of interest and 25 distractor objects) in comparison to a value of 24.15 achieved without such finetuning. the synthetic images were rendered with freely available 3D models with variations in parameters like color, texture and viewpoint without a high emphasis on photorealism. We analyze factors like training data set size, cue variances, 3D model dictionary size and network architecture for their influence on the transfer learning performance. Additionally, training strategies like fine-tuning with selected layers and early stopping which affect transfer learning from synthetic scenes to real scenes were explored. this approach is promising in scenarios where limited training data is available. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Network architecture

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Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray imagesg± 13

Performance, Trust, or both? COVID-19 Diagnosis and Prognosi...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Tiwari, Abhishek Singh, Rajeev Kumar Department of Computer Science and Engineering Shiv Nadar University Gautam Buddha Nagar UP Delhi India

ISBN: (纸本)9781450398237

the COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. the article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. the authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.

关键词： Coronavirus

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Fundus image Quality Assessment through Analysis of Illumination, Naturalness, and Structure Level 7th

Fundus Image Quality Assessment Through Analysis of Illumina...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Kanimozhi, J. Vasuki, P. Mohamed Mansoor Roomi, S. ECE Department K. L. N. College of Information Technology Sivagangai District PottapalayamTamilnadu India ECE Department Sethu Institute of Technology Kariyapatti VirudhunagarTamilnadu India ECE Department Thiagarajar College of Engineering MaduraiTamilnadu India

ISBN: (纸本)9789811586965

Optical diseases such as diabetic retinopathy, cataract and glaucoma generally have affected a more quantity of the populace international. image-based medical diagnosis relies upon adequate image quality and clarity. Mostly the fundus image acquisition are more vulnerable to distortions due to changes from a fixed place camera to portable fundus camera. the fundus images are the main tool for many retinal disease analyses. Repeated image acquisition is required for diagnosis because most of the images captured are of low quality. So, the automatic system is needed to evaluate the quality of the fundus image in terms of illumination level, naturalness level, and structure level. During the capturing time the Non-mydriatic image quality is more vulnerable to distortions. this kind of image quality distortions is called as generic quality distortions. the proposed work is about classification of the fundus image using the Adaptive Migration Biogeography based Optimization (AMBBO) Algorithm based Radial Basis Function Network (RBFN) where the Fundus image Quality is assessed through the Analysis of Illumination, Naturalness, and Structure level (FIQAINS) model. Changes have been proposed to the original BBO algorithm since it does not have the intrinsic property of clustering which is necessary in fundus image classification. Hence the modified algorithm is used to classify the fundus image based on the generic illumination feature, the critical naturalness feature and the necessary structure feature. the experimental results that make use of MATLAB tools to accomplish the generic overall quality classification such as sensitivity, specificity, accuracy and AUC and different threshold values from DRIMDB Dataset of fundus images and furthermore discover accept or reject class based on the AMBBO Algorithm. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Quality control

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Selective mixing and voting network for semi-supervised domain generalization 21

Selective mixing and voting network for semi-supervised doma...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Arfeen, Ahmad Dutta, Titir Biswas, Soma Indian Institute of Science Karnataka Bangalore India

ISBN: (纸本)9781450391276

Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain, by leveraging training samples from multiple source domains. Currently, the training process of the state-of-the-art DG-methods is dependent on a large amount of labeled data. this restricts the application of the models in many real-world scenarios, where collecting and annotating a large dataset is an expensive and difficult task. thus, in this paper, we address the problem of Semi-supervised Domain Generalization (SSDG), where the training set contains only a few labeled data, in addition to a large number of unlabeled data from multiple domains. this is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models, since their performance degrades under such condition. To address this scenario, we propose a novel Selective Mixing and Voting Network (SMV-Net), which effectively extracts useful knowledge from the set of unlabeled training data, available to the model. Specifically, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data, which generalizes effectively across any unseen domain. Secondly, we also propose a voting module, which not only improves the generalization capability of the classifier, but can also comment on the prediction of the test samples, using references from a few labeled training examples, despite of their domain gap. Finally, we introduce a test-time mixing strategy to re-look at the top class-predictions and re-order them if required to further boost the classification performance. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework. © 2021 ACM.

关键词： Large dataset

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Classification of breast tissue density 4th

Classification of breast tissue density

引用

4th International conference on computer vision and image processing, CVIP 2019

作者： Kashyap, Kanchan Lata Bajpai, Manish Kumar Khanna, Pritee VIT University Bhopal India Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9789811540172

Breast density classification plays an important role in breast cancer screening. Radiologists visually evaluate mammograms to classify it according to breast tissue density. In this work, automatic breast tissue density classification is presented which consists of preprocessing of mammograms, breast tissue segmentation, feature extraction from the segmented breast tissue and its classification based on the density. Mammogram preprocessing includes breast region extraction and enhancement of mammograms. Partial differential equation based variational level set method is applied to extract the breast region. Enhancement of mammograms is done by anisotropic diffusion. Further, breast tissues are segmented by applying clustering based technique. Texture based Local Ternary Pattern (LTP) and Dominant Rotated Local Binary Pattern (DRLBP) features are extracted in the subsequent step. Breast tissues are classified into 4- classes by applying support vector machine. the proposed algorithm has been tested on the publicly available 500 sample mammograms of Digital Database of Screening Mammography (DDSM) dataset. © Springer Nature Singapore Pte Ltd 2020.

关键词： Support vector machines

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Evolution of Bagbazar Street through Visibility Graph Analysis (1746–2020) 12th

Evolution of Bagbazar Street Through Visibility Graph Analys...

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12th indian conference on computer vision, graphics, and image processing, ICVGIP 2021

作者： Chakraborty, Shilpi Ji, Shiva Department of Design Indian Institute of Technology Hyderabad Telangana Sangareddy Kandi502284 India

ISBN: (纸本)9789811941351

Activities intended at increasing the accessibility of offers connected with urban growth and efforts and pragmatic practices targeted at boosting community contact through the usage of common areas contribute to cultural integration in the urban context. Bag Bazar Street is one of Kolkata’s three main streets, known as the grandmother road in ekantoporibar (joint family), making it an excellent case study site. By developing and assessing ideas regarding urban mobility characteristics and linkages across time, syntactic space analyses aid in the examination of diverse historical sources to build a coherent narrative of urban topology configuration. In a quantitative study of urban space and space syntax, enticing prospects as an exciting development of spaces use the city plan as the initial stage for investigating the historical relationship between the urban setting and urban life. the significant features of the axial line are with utilizing the argumentation of extrinsic spatial features in space syntax. DepthMap, open-source software for studying the visibility of urban core, was used in this study. the utilization of an axial map with a connection map is to frame basic facts in the study. Any strategy to revival or smart city development necessitates a full grasp of the spatial factors and stages that characterize our urban communities, as well as the link between the two. Consequently, this research uses axial analysis and visibility graph analysis to validate the degree of functionality of Kolkata’s Bag Bazar street over time as an input for digital preservation. the paper identifies the period having transformation for each attribute. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Urban growth

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Classification of hardwood species using ANN classifier

Classification of hardwood species using ANN classifier

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Arvind R. Yadav M. L. Dewal R. S. Anand Sangeeta Gupta Department of Electrical Engineering Indian Institute of Technology Roorkee Roorkee India Botany Division Forest Research Institute Dehradun Dehradun India

In this paper, an approach for the classification of different hardwood species of open access database, using texture feature extraction and supervised machine learning technique has been implemented. Edges of complex cellular structure of microscopic images of hardwood are enhanced with the application of Gabor filter, and Gray Level Co-occurrence Matrix (GLCM) as an effective texture feature extraction technique is being revalidated. About, 44 features have been extracted from GLCM; these features have been further normalized in the range [0.1, 1]. Multilayer Perceptron Backpropagation Artificial Neural Network have been used for classification. Experiments conducted on 25 wood species have resulted in recognition accuracy of about 88.60% and 92.60% using Levenberg-Marquardt backpropagation training function with two different datasets for training, validation and testing ratio (70%, 15%, 15% and 80%, 10%, 10%) respectively. Proposed methodology can be extended with optimized machine learning techniques for online identification of wood.

关键词： Feature extraction Training Accuracy Microscopy Gabor filters Backpropagation Artificial neural networks

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An Efficient Motor imagery Classification Framework using Sparse Brain Connectivity and Class-consistent Dictionary Learning from Electroencephalogram Signals 14

An Efficient Motor Imagery Classification Framework using Sp...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Pain, Subrata Samanta, Debasis Sarma, Monalisa Advanced Technology Development Center IIT Kharagpur West Bengal Kharagpur India Department of Computer Science and Engineering IIT Kharagpur West Bengal Kharagpur India Subir Chowdhury School of Quality and Reliability IIT Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

Identifying patterns in high-dimensional and noisy neuro-imaging data such as Electroencephalogram (EEG) has always been challenging. this fact hampers the performance of the real-time Brain-computer Interfaces (BCI) controlled by EEG recordings of brain signals, such as imagined motor activity (MI-EEG). Traditionally, the MI-EEG signals are classified by mainly two approaches, firstly, using the Common Spatial Patterns (CSPs) extracted from MI-EEG as features for Machine Learning (ML) classifiers, and secondly, using various Deep Learning (DL)-based approaches. But these methods are computationally heavy and ignore the dynamic topological interactions between the brain regions. Recently, due to high performance and low testing time, sparse representation-based classifiers have been utilized for MI-EEG classifications with higher real-life applicability. But these methods consider a fixed training dictionary consisting of inherently noisy training features. this study proposes an efficient MI-EEG classification framework utilizing a sparse representation of brain connectivity features and a dictionary learned from the training features. Here, the dynamic synchrony of co-activating brain regions is captured by suitable connectivity measures, which are further used as features. Further, sparse representations of the connectivity features are obtained through sparsity and dictionary learning. As this dictionary learning method preserves the class information during dictionary updation, this method is best suited for classification tasks. the dictionary learning method simultaneously learns a reduced dictionary and a linear classifier further used for classification. the proposed framework is validated using a publicly available MI dataset, and corresponding classification performances are observed. the proposed framework outperforms the other sparsity-based classifiers in mixed-, intra-, and cross-subject performances, making it suitable for real-life BCI applications.

关键词： Electroencephalography

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Tensor based dictionary learning for compressive sensing MRI reconstruction 4th

Tensor based dictionary learning for compressive sensing MRI...

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4th International conference on computer vision and image processing, CVIP 2019

作者： Mubarak, Minha thomas, thomas James Sheeba Rani, J. Mishra, Deepak Department of Avionics Indian Institute of Space Science and Technology ThiruvananthapuramKerala695547 India

ISBN: (纸本)9789811540141

Compressed Sensing (CS) has heralded a new era in cardiac and body Magnetic Resonance (MR) imaging due to the unprecedented acceleration factors achieved within the ambit of existing scanning hardware, by exploiting the inherent sparsity of MR images in a known basis. Latter works have adopted dictionary learning strategies on vectorized image patches to enforce higher degrees of sparsity. However, the enormous computational complexity has confined them to learning from small patches, thereby foregoing any consideration of the expected structure of the MR images from prior knowledge. In order to efficiently exploit the multi-dimensional characteristic of MR data, this work learns 2D separable dictionaries possessing Kronecker structure at low computational costs by adapting the CANDECOMP/PARAFAC (CP) tensor decomposition method on stacked 2D under-sampled MR slices of similar scan type. the proposed method has been observed to obtain superior reconstruction quality in noiseless and noisy acquisition scenarios over the state-of-art. Furthermore, the learned dictionary can jointly reconstruct a stack of distinct 2D under-sampled slices of similar scan type, in significantly reduced running time. © Springer Nature Singapore Pte Ltd 2020.

关键词： Magnetic resonance

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