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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia Computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) Computer-aided diagnosis (CAD)

A Comparative Analysis of Deep Learning Architectures for Efficient Brain Tumor Detection

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A Comparative Analysis of Deep Learning Architectures for Ef...

Sustainable Technology and engineering (i-COSTE), International Conference on

作者： Muhammad Zohaib Khan Fahim Uz Zaman Priha Bhatti Abdullah Ayub Khan Syed Akmal Sultan Shehzeen Dua Bhatti Department of Information Technology Shaheed Mohtarma Benazir Bhutto Institute of Trauma Karachi Pakistan Department: Digital Technologies Newcastle College University Centre (NCUC) Newcastle upon Tyne United Kingdom Department of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Department of Computer Science Bahria University Karachi Pakistan Department of Orthopedic Surgery Shaheed Mohtarma Benazir Bhutto Institute of Trauma Karachi Pakistan Bachelor of Medicine & Bachelor of Surgery Dow University of Health Sciences Karachi Pakistan

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

This article studies the effectiveness of deep learning (DL) algorithms in detecting brain tumors, focusing on disorders such as “Glioma-Tumor,” “Meningioma-Tumor,” “PituitaryTumor,” and “No-Tumor.” Magnetic Resonance Imaging (MRI) is the primary tool for identifying brain tumors, and the paper proposes a convolutional neural network (CNN) architecture for efficient tumor detection. The study explores various CNN models, including DenseNet121, ResNet50V2, DenseNet201, EfficientNetB2, VGG16, and MobileNet, which enhance classification accuracy. The models demonstrate high precision, recall, F1-score, sensitivity, and specificity in predicting brain tumor conditions.

关键词： Deep learning Accuracy Magnetic resonance imaging Scalability Brain tumors Sensitivity and specificity Predictive models Brain modeling Neurosurgery Convolutional neural networks

Generalized Cross-Entropy Benchmarking for Random Circuits with Ergodicity

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arXiv 2025年

作者： Cheng, Bin Meng, Fei Zhang, Zhi-Jiong Yung, Man-Hong Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Technologies National University of Singapore Singapore Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Physics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Cross-entropy benchmarking is a central technique used to certify a quantum chip in recent experiments. To better understand its mathematical foundation and develop new benchmarking schemes, we introduce the concept of ergodicity to random circuit sampling and find that the Haar random quantum circuit satisfies an ergodicity condition—the average of certain types of post-processing function over the output bit strings is close to the average over the unitary ensemble. For noiseless random circuits, we prove that the ergodicity holds for polynomials of degree t with positive coefficients and when the random circuits form a unitary 2t-design. For strong enough noise, the ergodicity condition is violated. This suggests that ergodicity is a property that can be exploited to certify a quantum chip. We formulate the deviation of ergodicity as a measure for quantum chip benchmarking and show that it can be used to estimate the circuit fidelity for global depolarizing noise and weakly correlated noise. For a quadratic post-processing function, our framework recovers Google’s result on estimating the circuit fidelity via linear cross-entropy benchmarking (XEB), and we give a sufficient condition on the noise model characterizing when such estimation is valid. Our results establish an interesting connection between ergodicity and noise in random circuits and provide new insights into designing quantum benchmarking schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum electronics

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Deep Superpixel Cut for Unsupervised Image Segmentation

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Deep Superpixel Cut for Unsupervised Image Segmentation

International Conference on Pattern Recognition

作者： Qinghong Lin Weichan Zhong Jianglin Lu Institute of Computer Mathematics and Information Technologies Kazan Federal University Kazan Russia College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the great success of deep learning technology, CNNs based methods show superior performance in image segmentation. However, these methods rely on a large number of human annotations, which are expensive to collect. In this paper, we propose a deep unsupervised method for image segmentation, which contains the following two stages. First, a Superpixelwise Autoencoder (SuperAE) is designed to learn the deep embedding and reconstruct a smoothed image, then the smoothed image is passed to generate superpixels. Second, we present a novel clustering algorithm called Deep Superpixel Cut (DSC), which measures the deep similarity between superpixels and formulates image segmentation as a soft partitioning problem. Via backpropagation, DSC adaptively partitions the superpixels into perceptual regions. Experimental results on the BSDS500 dataset demonstrate the effectiveness of the proposed method.

关键词： Deep learning Backpropagation Image segmentation Annotations Clustering methods Clustering algorithms Partitioning algorithms

Ship detection in marine radar images based on a modified YOLOv3-tiny 5

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Ship detection in marine radar images based on a modified YO...

IET International Radar Conference 2020, IET IRC 2020

作者： Lei, Yuncong Si, Lingyu Sun, Liguo Xu, Fanjiang Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou China

ISBN: (纸本)9781839535635

Deep learning has shown great success on object detection task for optical images, yet it's rarely used in marine radar target detection. It is mainly due to the scarce of the labelled marine radar datasets for deep learning. To mitigate such effect, we build a pseudo-color marine radar dataset, which utilizes rich temporal information to remarkably enhance the accuracy of deep radar detectors. Meanwhile, the state-of-the-art one stage deep detector, YOLO family, is transferred into this field. Eventually, we modify the architecture of YOLOv3-tiny by attaching the prediction heads to shallower layers. Based on the pseudo-color dataset, our method, Shallow YOLOv3-tiny, is able to match the speed and model size of the standard YOLOv3-tiny while improving the accuracy significantly. Thus, our method is excellent at performing radar target detection tasks on embedded devices with low computing power and limited memory. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Object detection

Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering

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arXiv 2023年

作者： Jasso, Alonso Viladomat Modi, Ark Ferrara, Roberto Deppe, Christian Nötzel, Janis Fung, Fred Schädler, Maximilian Theoretical Quantum System Design Group Theoretical Information Technology Technical University of Munich Munich80333 Germany Institute for Communications Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Optical and Quantum Laboratory Munich Research Center Huawei Technologies Düsseldorf GmbH Riesstr. 25-C3 Munich80992 Germany

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm;however, it has been shown to not currently provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed ‘quantum-inspired’ algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms;here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate. © 2023, CC BY.

关键词： Embeddings

Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

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Frontiers of information Technology & Electronic engineering 2019年第2期20卷 253-264页

作者： Ting-ting JIN Xiao-qiang SHE Xiao-lan QIU Bin LEI Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Huawei Software Technologies Co. Ltd.

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

关键词： Intertidal classification Polarimetric synthetic aperture radar Finite mixture model Markov random field Generalized extreme value model

Distributed exact multi-objective quantum search algorithm

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arXiv 2024年

作者： Li, Hao Qiu, Daowen Luo, Le Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Shenzhen Research Institute of Sun Yat-Sen University Nanshan Shenzhen518057 China QUDOOR Technologies Inc. China School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China

Multi-objective search means searching for any one of several objectives in an unstructured database. Grover’s algorithm has quadratic acceleration in multi-objection search than classical ones. Iterated operator in Grover’s algorithm is a key element and plays an important role in amplitude amplification. In this paper, we design two distributed iterated operators and therefore two new distributed Grover’s algorithms are obtained with the following advantages: (1) Compared to Grover’s algorithm and the modified Grover’s algorithm by Long, our distributed algorithms require fewer qubits;(2) Compared to the distributed Grover’s algorithm proposed by Qiu et al., one of our distributed algorithms is exact. Of course, both our distributed algorithms require quite quantum communication and involve a number of more complicated unitary operators as cost, but there still may have certain advantage of physical realizability in the Noisy Intermediate-Scale Quantum (NISQ) era. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm