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Revolutionizing Alzheimer's Detection: Hybrid Deep Learning Models in Experimental Analysis and Insights 8

Revolutionizing Alzheimer's Detection: Hybrid Deep Learning ...

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8th international conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Praveena, A. Kalpana, Y. Vel's Institute of Technology & advanced Studies Department of Computer Science Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Dept of Applied computing and Emerging Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331544607

the detection and diagnosis of Alzheimer's Disease (AD) are critically important due to the increasing prevalence of the condition, especially among aging populations. this study investigates the enhancement of AD detection through hybrid deep learning models, utilizing a dataset from Kaggle comprising images categorized into four stages: Mild Demented, Very Mild Demented, Non-Demented, and Moderate Demented. We developed two innovative models: the FusionTree Classifier (FTC) and the FusionNet Integrator (FNI). the FTC combines Random Forest and Bagging techniques to reduce variance and overfitting, achieving an impressive accuracy of 96.78%. this hybrid approach leverages the strengths of ensemble learning methods, enhancing robustness and reliability in classification tasks. On the other hand, the FNI integrates VGG's powerful feature extraction capabilities with the classification strength of Support Vector Machines (SVMs), effectively identifying complex image patterns and achieving an even higher accuracy of 97.41%. By combining convolutional neural networks (CNNs) for feature extraction with SVMs for classification, FNI capitalizes on the strengths of both techniques, demonstrating superior predictive accuracy compared to traditional methods. these advancements hold significant promise for improving diagnostic accuracy and patient outcomes in AD and other neurological conditions. the methodologies proposed in this study offer a reliable framework for early detection, which is crucial for timely intervention and management of the disease. © 2025 IEEE.

关键词： Support vector machines

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Comparative analysis of fuzzy C-Means and K-Means clustering in the case of image segmentation 8

Comparative analysis of fuzzy C-Means and K-Means clustering...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, Arvind Sodhi, Sartaj Singh Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544434

image segmentation is an essential step in image processing and analysis. Neural Network, Fuzzy C-Means and K-Means clustering techniques are very famous segmentation techniques. Neural Network is best for segmentation but only classifies trained images. For every image, we do not apply the neural network. Further, we check which other two types of segment techniques are best. For this purpose, we apply these two types of segment techniques on different images. the dice and bfscore performance were estimated for these algorithms and found that if the image is less complex, then Fuzzy C-Means gives better result. If the image is more complex, then K-Means clustering gives better result. In the case of medical data, K - Means clustering is fast. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： image segmentation

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Over-the-RIS Modulation: A Reconfigurable Intelligent Surface assisted Modulation Design

Over-the-RIS Modulation: A Reconfigurable Intelligent Surfac...

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Communication, image and Signal Processing (CCISP), international conference on

作者： Yiqian Huang Saviour Zammit Ping Yang Yue Xiao Bo Zhang Jienan Chen Wei Xiang National Key Laboratory of Sciencec and Technology on Communications University of Electronic Science and Technology of China Msida China Department of Communications and Computer Engineering University of Malta Sichuan Malta 32806 Troops Beijing China School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne Australia

this paper proposes an improved over the air modulation (OTAM) scheme by using the powerful reconfigurable intelligent surfaces (RISs), namely over-the-RIS modulation (ORM). In the design framework, we reconstruct multipath channels by employing several RISs to increase the attenuation difference between multiple paths. Compared with conventional OTAM, ORM can be used in more generic fading scenarios, e.g., channels with non-ideal attenuation properties, and thus extends the application of OTAM. Based on the Bayesian criterion, we propose an optimal likelihood detector for the ORM system and derive a closed-form expression of the bit error rate (BER). Furthermore, we investigate the phase optimization of the RISs to improve its BER and deduce its closed-form solutions. Simulation results are presented to show that the proposed ORM system can achieve a better BER performance and higher transmission rate than the conventional OTAM systems, especially for environments with severe shadow fading.

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An optimization r-cnn method for Ovarian cyst detection

An optimization r-cnn method for Ovarian cyst detection

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IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Jiade Li Qili Chen Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665456579

With the continuous development and progress of medical imaging technology and computer technology, medical image analysis has become an indispensable tool and technical means in medical research, clinical disease diagnosis and treatment. this paper will introduce the ovarian cyst detector based on r-cnn and its improved optimization method, and make a comparative analysis..

关键词： Cloud computing image analysis Optimization methods Detectors medical diagnosis Intelligent systems medical diagnostic imaging

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Interactive Semantic Segmentation With Weak Supervision 22

Interactive Semantic Segmentation With Weak Supervision

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781450396110

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. the weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. the proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets. © 2022 ACM.

关键词： Semantic Segmentation

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Advancements and Innovations in U-Net for Enhanced medical image Segmentation: A Review

Advancements and Innovations in U-Net for Enhanced Medical I...

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Mechanical Engineering and Robotics Research (ICMERR), international conference on

作者： Zihan Deng Shaohan Yang Xindi Zhang Nan Xiang Department of Computing School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

As one of the most prominent network architectures in recent years within the field of medical image segmentation, U-Net has gained widespread adoption due to its exceptional performance, with various extensions continually emerging. Recently, the rapid progress of U-Net has led to a flood of new methods and uses every year. In this context, it becomes imperative to undertake reviews and encapsulate the latest advancements. Such efforts are vital for offering an updated and comprehensible standpoint to researchers interested in this field. this paper highlights the most recent variants of the U-Net, reviews their principles, and summarizes their successful applications by their optimization approaches in medical segmentation tasks. Furthermore, with the success of Transformers in the field of computer vision, there have been many enthusiastic efforts in recent years to apply self-attention mechanisms to medical image segmentation. this paper delves extensively into how the Transformer model empowers the evolution of the U-Net framework in the context of medical image segmentation. Moreover, it also investigates specific use cases of U-Net in micro and macro tasks such as nucleus segmentation, lung and its nodule, liver segmentation, and gastric detection, all of which hold profound significance in clinical diagnostics. Based on comprehensive literature investigation and analysis, this study provides a forward-looking perspective on the future advancement and possible challenges that U-Net could confront.

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HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT images 3rd

HNT-AI: An Automatic Segmentation Framework for Head and N...

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

作者： Salahuddin, Zohaib Chen, Yi Zhong, Xian Rad, Nastaran Mohammadian Woodruff, Henry C. Lambin, Philippe The D-Lab Department of Precision Medicine GROW-School for Oncology and Reproduction Maastricht University Maastricht Netherlands Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis College of Computer Science and Technology Guizhou University Guiyang China Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound The First Affiliated Hospital of Sun Yat-sen University Guangzhou China Department of Radiology and Nuclear Medicine GROW-School for Oncology Maastricht University Medical Center Maastricht Netherlands

ISBN: (纸本)9783031274190

Head and neck cancer is one of the most prevalent cancers in the world. Automatic delineation of primary tumors and lymph nodes is important for cancer diagnosis and treatment. In this paper, we develop a deep learning-based model for automatic tumor segmentation, HNT-AI, using PET/CT images provided by the MICCAI 2022 Head and Neck Tumor (HECKTOR) segmentation Challenge. We investigate the effect of residual blocks, squeeze-and-excitation normalization, and grid-attention gates on the performance of 3D-UNET. We project the predicted masks on the z-axis and apply k-means clustering to reduce the number of false positive predictions. Our proposed HNT-AI segmentation framework achieves an aggregated dice score of 0.774 and 0.759 for primary tumors and lymph nodes, respectively, on the unseen external test set. Qualitative analysis of the predicted segmentation masks shows that the predicted segmentation mask tends to follow the high standardized uptake value (SUV) area on the PET scans more closely than the ground truth masks. the largest tumor volume, the larget lymph node volume, and the total number of lymph nodes derived from the segmentation proved to be potential biomarkers for recurrence-free survival with a C-index of 0.627 on the test set. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biomarkers

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AlignFuse: Multi-feature Alignment and Fusion for medical image Segmentation

AlignFuse: Multi-feature Alignment and Fusion for Medical Im...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Shengyang Chao Zijun Liu Lili Huang Yanping Fu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained.

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Self-supervised Multi-modality image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer 2nd

Self-supervised Multi-modality Image Feature Extraction for ...

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

作者： Ma, Baoqiang Guo, Jiapan De Biase, Alessia Sourlos, Nikos Tang, Wei van Ooijen, Peter Both, Stefan Sijtsema, Nanna Maria RB Groningen9700 Netherlands

ISBN: (纸本)9783030982522

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. the proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. the whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. the C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints. © 2022, Springer Nature Switzerland AG.

关键词： Pipelines

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Detection of the Crown of thorns starfish using YOLOv5

Detection of the Crown of Thorns starfish using YOLOv5

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Pankaj Kunekar Pradnya Vishal Patil Babeetta Bbhagat Tejal H. Patil Neha Rajas Pratiksha Yuvraj Sawant Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra India Computer Engineering Pillai HOC college of engineering Rasayani MIT School of Computing Pune Maharashtra India Department of AI&DS Sinhgad College of Engineering Pune Maharashtra India Department of AI&DS Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science PVG’s College of Science and Commerce Pune Maharashtra India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

the invasive Crown-of-thorns starfish (COTS) poses a significant threat to coral reef ecosystems. Early detection and removal are crucial to mitigating their impact. this research proposes a YOLOv5-based object detection system to accurately identify and locate COTS in underwater images. By fine-tuning the YOLOv5 model with a dataset of COTS images, the system can effectively detect and classify COTS with high precision and recall. the output of the system includes bounding boxes and confidence scores for each detected COTS, enabling timely intervention and removal efforts. this research contributes to the conservation of coral reefs by providing a valuable tool for monitoring and controlling COTS populations. the proposed system can be integrated into underwater drones or diveroperated devices to facilitate efficient and accurate COTS detection and removal.

关键词： YOLO Accuracy Ecosystems Marine vegetation Network architecture Object recognition image preprocessing Optimization Monitoring image classification

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