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Deep learning-based computer aided diagnosis model for skin cancer detection and classification

distributed AND parallel DATABASES 2022年第4期40卷 717-736页

作者： Adla, Devakishan Reddy, G. Venkata Rami Nayak, Padmalaya Karuna, G. JNTUH Hyderabad India VJIT Dept IT Hyderabad India JNTUH Sch Informat Technol Hyderabad India GRIET Dept CSE Hyderabad India JNTUH Dept CSE GRIET Hyderabad India

Skin cancer is a commonly occurring disease, which affects people of all age groups. Automated detection of skin cancer is needed to decrease the death rate by identifying the diseases at the initial stage. The visual inspection during the medical examination of skin lesions is a tedious process as the resemblance among the lesions exists. Recently, imaging-based Computer Aided Diagnosis (CAD) model is widely used to screen and detect the skin cancer. This paper is designed with automated Deep Learning with a class attention layer based CAD model for skin lesion detection and classification known as DLCAL-SLDC. The goal of the DLCAL-SLDC model is to detect and classify the different types of skin cancer using dermoscopic images. During image pre-processing, Dull razor approach-based hair removal and average median filtering-based noise removal processes take place. Tsallis entropy based segmentation technique is applied to detect the affected lesion areas in the dermoscopic images. Also, a DLCAL based feature extractor is used for extracting the features from the segmented lesions using Capsule Network (CapsNet) along with CAL and Adagrad optimizer. The CAL layer incorporated into the CapsNet is intended to capture the discriminative class-specific features to cover the class dependencies and effectively bridge the CapsNet for further process. Finally, the classification is carried out by the Swallow Swarm Optimization (SSO) algorithm based Convolutional Sparse Autoencoder (CSAE) known as SSO-CSAE model. The proposed DLCAL-SLDC technique is validated using a benchmark ISIC dataset. The proposed framework has accomplished promising results with 98.50% accuracy, 94.5% sensitivity and 99.1% specificity over the other methods interms of different measures.

关键词： Skin lesion Dermoscopic images image classification ISIC dataset Deep learning image segmentation Hair removal Capsule Networks

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Spatial-Frequency Integration Network with Dual Prompt Learning for Few-shot image Classification

Spatial-Frequency Integration Network with Dual Prompt Learn...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yi Liu Jie He Shoukun Xu Guangqi Jiang Academy of Computing and Artificial Intelligence Changzhou University Changzhou 213164 Jiangsu China Changzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Few-shot image classification is a challenging task that aims to recognize image classes based on only a few training images. However, existing methods face the following two main challenges: (1) Ignoring the frequency domain information during image feature extraction. (2) It does not take the semantic gap between multiple modalities into consideration, which limits the classification performance. To overcome these limitations, we propose a novel method named Spatial-Frequency Integration Network with Dual Prompt Learning for few-shot image classification. Firstly, we introduce a spatial-frequency integration module that combines spatial domain and low-frequency information to extract discriminative image features from the image modality. Secondly, we design a dual prompting module, which integrates learnable prompts and hand-crafted prompts to improve the generalization of applications to new classes. Thirdly, we propose an image-text interaction module to enhance inter-modal complementary and consistency. Both theoretical and experimental validations confirm the effectiveness of the proposed method in few-shot image classification.

关键词： Training image segmentation image recognition Frequency-domain analysis Face recognition Semantics Object detection Feature extraction Few shot learning image classification

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parallel Coregistration Algorithm For Sar images Based On Hadoop

Parallel Coregistration Algorithm For Sar Images Based On Ha...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiawei Li Guobing Zeng Huaping Xu School of Electronic and Information Engineering Beihang University Beijing China

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

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On GPU optimizations of stencil codes for highly parallel simulations 29

On GPU optimizations of stencil codes for highly parallel si...

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29th Euromicro International Conference on parallel, distributed and Network-Based processing (PDP)

作者： Pfisterer, Nikolai Berghoff, Marco Streit, Achim Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany

ISBN: (纸本)9781665414555

Stencil codes are valuable methods to solve partial differential equations of models in a wide range of applications in science and engineering. Graphics processing units (GPUs) provide a highly parallel architecture with fast directly accessible memory that is desirable to run stencil codes. They enable larger and more complex simulations that are solved faster compared to simulating on CPUs. We provide a solution for users to run highly parallel stencil codes on GPUs, that can also be efficiently used on a distributed GPU cluster. In this work, we present an extension to the multi-disciplinary framework NAStJA originally designed to efficiently run stencil codes on the CPUs in current high-performance computing (HPC) systems, to also be able to run on GPUs. We describe different methods to increase the performance of stencil codes on GPUs like a border exchange method which is transparent to the user, as well as a buffer for gradient values that are needed multiple times. We show their performance using the phase-field method as a stencil code example. With this GPU extension and optimizations implemented into the NAStJA framework, we can show highly improved performance compared to the CPU implementation, and an efficiency of 92% (weak scaling) for a large-scale example simulation on 64 GPUs on the ForHLR ii HPC system.

关键词： GPU optimizations stencil code heterogeneous parallelization scalability analysis scalability analysis

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High-Precision Encrypted image Retrieval Scheme Using Dual-Stream Convolution Feature Fusion in Cloud Computing

High-Precision Encrypted Image Retrieval Scheme Using Dual-S...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Mingyue Li Liudong Zheng Jiahui Liu Ruizhong Du Chunfu Jia Hebei University Baoding China Nankai University Tianjin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The retrieval of encrypted images in cloud computing is a research hotspot at present. However, the existing schemes have the problem of low image retrieval accuracy since it is difficult to obtain accurate feature information through convolutional neural network from encrypted image. In this paper, a secure image retrieval method using dual-stream convolution structure for feature fusion is proposed. Specifically, the stream cryptographic image containing contour features and the Fourier transform image containing frequency domain features are used as the two inputs of the convolutional network stream, where the weighted average gate function helps to integrate the feature information in these streams to achieve a more comprehensive feature representation and improve the retrieval accuracy. Furthermore, to keep the contour features of the encrypted image, the fuzzy image and Arnold mapping algorithm are selected randomly to encrypt the encrypted image twice. Experimental evaluation on three datasets, MNIST, Fashion-MNIST and CIFAR-100, shows that the proposed encryption method has higher security than AES and stream cipher encryption. In addition, the retrieval average accuracy (mAP) of this scheme is superior to the existing methods.

关键词： Cloud computing Ciphers Accuracy Convolution image retrieval Streaming media Logic gates Encryption Convolutional neural networks Servers

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STA-VAD: Video Anomaly Detection Utilizing Spatio-Temporal Memory and Adaptive Deformation

STA-VAD: Video Anomaly Detection Utilizing Spatio-Temporal M...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Huiyu Mu Jiashuai Su Luhui Wang Jiangwei Li Yadi Wang Xianyu Zuo Henan University Kaifeng China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Classical unsupervised video anomaly detection methods learn normal patterns from normal video frames and assume significant reconstruction deviations for unforeseen anomalous video frames. These methods often introduce additional constraints to distinguish anomalies, potentially leading to higher reconstruction errors for normal instances. While specific methods have alleviated this issue by estimating multiscale deformation fields, these rely on additional background information that is susceptible to changes in the real world. In this paper, we propose a spatio-temporal relationship-based video anomaly detection STA-VAD method. By emphasizing spatio-temporal relationships in videos, our model better captures contextual information, reducing the impact of irrelevant background information. Additionally, we enhance the multiscale deformation field by adaptively adjusting it to quantify the severity of anomalies in video frames, thereby improving discrimination between normal and anomalous samples. In the widely recognized video anomaly detection datasets UCSD Ped2 and CUHK Avenue, our method demonstrates significant enhancements in accuracy and reliability, achieving state-of-the-art performance overall.

关键词： Adaptation models image recognition Deformation Interference Feature extraction Reliability image reconstruction Anomaly detection Standards Optical flow

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Optimizing Transitive Closure Computation for High Performance Computing and Security

Optimizing Transitive Closure Computation for High Performan...

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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Elio Masciari Enea Vincenzo Napolitano DIETI University of Naples Federico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security.

关键词： Heuristic algorithms High performance computing Scalability Memory management Data structures Real-time systems Computational efficiency Security Sparse matrices Optimization

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Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised image Segmentation

Inverse Quantum Fourier Transform Inspired Algorithm for Uns...

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Taoreed Akinola Xiangfang Li Richard Wilkins Pamela Obiomon Lijun Qian Department of Electrical and Computer Engineering Prairie View A&M University Prairie View Texas USA

image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels’ intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsu-thresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).

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ESNet: An Efficient Real-time Semantic Segmentation Network

ESNet: An Efficient Real-time Semantic Segmentation Network

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Renping Xie Cong He Ming Tao Kai Ding School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Efficient image segmentation algorithms are critical in computer vision, as they maintain high processing speeds while handling large amounts of data and providing practical solutions in resource-limited environments. While existing classical segmentation methods achieve good results, their real-time performance can be further improved. To address this issue, we propose an efficient segmentation network (ESNet) to capture extensive contextual information and improve segmentation fineness, which are two main issues in semantic segmentation, while ensuring real-time capability. Then, we first introduce an efficient context module (ECM) that effectively enlarges the effective receptive field (ERF) of the model and improves its performance in integrating contextual information. Subsequently, a skip connection with a simple feature fusion module (SFFM) is designed to provide rich detail information, thereby improving segmentation fineness. The efficacy of ESNet was evaluated on the PASCAL VOC2012 semantic segmentation dataset against several state-of-the-art methods. ESNet achieves 71.4 FPS at a resolution of 1024×2048 on a single NVIDIA GeForce RTX 3090 GPU and achieves 62.42% mIoU at a resolution of 320×480 with an extremely simple training recipe, striking a fine balance between segmentation accuracy and inference speed.

关键词： Training distributed processing Computer vision Accuracy Semantic segmentation Computational modeling Graphics processing units Rendering (computer graphics) Real-time systems Context modeling

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A Normalized Mutual Information-based Multi-Modal Medical image Fusion Technique using Non Sub-sampled Shearlet Transform and Method Noise

A Normalized Mutual Information-based Multi-Modal Medical Im...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In image processing, image fusion is the process of merging characteristics and information from multiple images to build a single, more complete representation. This approach includes multi-modal medical image fusion, which involves fusing medical images from several imaging modalities such as computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI). This integration improves diagnostic accuracy by using each modality's own strengths, resulting in clearer visualization of anatomical structures and clinical situations. This work utilizes CT, PET, and MRI images as modalities for the experimentation purpose. This study (M3IF-NSST-MI) aims to construct a multi modal medical image approach that combines Non Sub-sampled Shearlet Transform (NSST) and method noise to the information content in the fused image, where high pass subbands are fused using Normalized Mutual Information (NMI) based fusion strategy. NSST domain is used for its Multi-Scale Representation and shift invariance. The concept of method noise as post-processing operation that extracts the information which was missed in the initial phase. The wavelet coefficients are fused using NMI-based fusion method. This combination of NSST with NMI-based fusion and method noise provides superior results than many conventional methods. This improves overall visual and parametric quality of fusion results than many compared methods.

关键词： Visualization Accuracy Magnetic resonance imaging Computed tomography Noise Positron emission tomography Medical diagnostic imaging image fusion Mutual information Wavelet coefficients

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