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Exploring Meibomian Gland Dysfunction Grading: A Comparison of Machine Learning and XAI Approaches 2

Exploring Meibomian Gland Dysfunction Grading: A Comparison ...

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2nd IEEE International conference on image Processing and Computer Applications, ICIPCA 2024

作者： Liang, Congxiao Wang, Mini Han Liu, Haoyang Chong, Kelvin K.L. Beijing Institute of Technology Center for Sport Science and Physical Education Zhuhai519000 China The Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong 999077 Hong Kong The Faculty of Data Science City University of Macau Macau999078 China The Center of Big Data and Ai Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China The R&d Center of Ai and Digital Medicine Guangdong Digital Medicine Promotion Science and Technology Co. Ltd Guangdong 519000 China Guangdong Medical University Dongguan China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China The Faculty of Medicine Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350360240

This research investigates the effectiveness of employing a machine learning methodology and an Explainable Artificial Intelligence (XAI) tool for the assessment of Meibomian Gland Dysfunction (MGD). Utilizing ResNet34, a convolutional neural network (CNN), trained on a diverse dataset comprising images depicting varying degrees of MGD severity, the machine learning approach endeavors to classify MGD severity into four distinct grades (0, 1, 2, 3) by leveraging extracted characteristic features. Findings reveal comparable accuracy between ResNet34 and the XAI tool, with ResNet34 achieving a slightly lower accuracy of 99.1% in contrast to the XAI tool's 99.4%. However, ResNet34 demonstrates marginally higher precision and F1-score at 98.2% and 98.45%, respectively, suggesting a nuanced advantage in precision-recall equilibrium. Conversely, the XAI tool maintains a commendable precision of 97.2% and an Fl-score of 97.94%, alongside a recall of 98.7%, underscoring its effectiveness in discerning positive instances. Noteworthy is that ResNet34 operates as a black-box model, providing limited interpretability, while the XAI tool emphasizes transparency by furnishing clinicians with understandable MGD grading elucidations derived from meibomian gland segmentation and atrophy analysis. This transparency enhances trust and comprehension, which are integral for clinical acceptance and decision-making. Furthermore, the utilization of XAI heatmap generation facilitates data quality enhancement, as evidenced by instances of erroneous artifact identification, emphasizing the imperative nature of artifact elimination for the refinement of MGD grading procedures. © 2024 IEEE.

关键词： Convolutional neural networks

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2nd EAI International conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

2nd EAI International Conference on Pervasive knowledge and ...

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2nd EAI International conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

ISBN: (纸本)9783031660436

The proceedings contain 28 papers. The special focus in this conference is on Pervasive knowledge and collective intelligence on Web and Social Media. The topics include: A Systematic Review: Remote Sensed Hyperspectral image Segmentation and Caption Generation Using Deep Learning Methods;Cyber Sentinels: Illuminating Malicious Intent in Social Networks Using Dual-Powered CHAM;Detecting and Preventing DoS Attacks Within IoT Environment Using AWS IoT Core;a Novel Technique to Enhance the Visibility of Sand-Dust images;citation Analysis of a Q1 Journal from Its Thirty Years of Inception in Agriculture Supply-Chain;IGSentiment Analysis of Russia and Ukraine War on Twitter data: Using Azure Machin Learning and Deep Learning;revolutionizing Autonomous Vehicle Intelligence with Cutting-Edge Spatial Crowdsourcing Framework;identification of Spam on Social Media by Semi-supervised Learning Approach;logistics Shipping Based Blockchain Using Smart Contracts;image Denoising Using AI with Entropy as Metric Analysis;enhancing Health Record Security and Privacy with Blockchain-Based Access Management;socialChain: A Decentralized Social Media Platform on the Blockchain;behavioural Analysis in Web Pattern Mining of Social Media Networks Using Deep DenseNet Classification;identification of Wild Animals in Forest Surveillance Cameras;social Media Impact on Student’s Academic Performance: A Study of College Students in Bhubaneswar;semantic Web with Block Chain Technology Parsing New Trends to Extract Novel Scientific Demands;loan Status Prediction System with Ensembled Machine Learning Models: Elevating Information Reliability and Accuracy;effect of Global Warming on Glaciers, Rivers and Water Towers;social Media Toxic-Text Analysis Using Deep Learning Techniques;a Survey on Twitter Sentiment Analysis Using Machine Learning Techniques.

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Detecting Mango Leaf Disease with VGG 16 Transfer Learning Model 2

Detecting Mango Leaf Disease with VGG 16 Transfer Learning M...

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2nd IEEE International conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Singh, Rahul Sharma, Neha Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350381979

Mango leaf disease is a major issue affecting worldwide mango trees. If left untreated, it can cause substantial damage to the leaves and potentially cause the tree to die. The importance of early discovery in controlling the spread of this disease and minimizing its impact on the mango tree population cannot be overstated. The dataset contains 4000 photos separated into eight distinct classifications. Each class reflects a different type of mango leaf disease. The dataset has two types of data: training data and validation data. There are 3200 photos in the training data and 800 images in the validation data. This article investigated the significance of early detection of mango leaf disease and the possible application of the VGG 16 Transfer Learning Model to solve this issue. Our study in illness detection has achieved encouraging results through systematically collecting and preprocessing data, training a model with carefully selected hyperparameters, and evaluating the model's performance. Our work is very accurate, with a 97% accuracy rate. Using the VGG 16 Transfer Learning Model for mango leaf disease identification can drastically reduce the time and effort necessary for human inspection methods. This is especially crucial for large-scale agricultural companies. Aside from its multiple advantages, this technology has the potential for better precision and accuracy in assessing crop problems, allowing for enhanced farming practices and increased crop output. © 2023 IEEE.

关键词： image classification

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Modified Intuitionistic Fuzzy Clustering Method (MIFCM) for Microarray image Spot Segmentation 2

Modified Intuitionistic Fuzzy Clustering Method (MIFCM) for ...

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2nd International conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Priya, M.P. Roopa, C.K. Harish, B.S. Department of Information Science and Engineering JSS Science and Technology University MysuruKarnataka State 570006 India

The study of biotic nomenclature has resulted from gene expression profiling using integrated image analysis techniques. The study emphasized in this paper confers Modified Intuitionistic Fuzzy Clustering Method (MIFCM) for spot segmentation of microarray image. In order to achieve the basic condition of intuitionism, a generator using Intuitionistic Fuzzy set (IFS) is used. MIFCM approach considers degree of uncertainty, value of association and value of non-association to determine similarity between the microarray spot data points which further helps in clustering. For non-association value calculation, Sugeno's [47] and Yager's [30][44] fuzzy complement generator [49] with intuitionism approach is proposed in this work. To achieve elementary level of intuitionism [49], MIFCM is integrated with Hausdorff distance metrics [43]. The Hausdorff distance metrics is used to quantify the distance metrics betwixt microarray image pixel and cluster centers. The study discusses the constraints of the evolutionary Fuzzy C-Means (FCM) method [4] and adoption of new fuzzy complement generator which overcomes limitations to increase the clustering efficiency. Further, the paper presents detailed methodology of calculating the parameters required to convert crisp spot data of microarray image into fuzzy set which will be input to the next stage of kernel induced clustering using MIFCM technique. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gene expression

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Phenotypic Parameters Extraction of Panax notoginseng with NeRF 12

Phenotypic Parameters Extraction of Panax notoginseng with N...

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12th International conference on Information Systems and Computing Technology

作者： Wang, Ruoxi Dai, Liang Yang, Qiliang Yang, Ling Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China

ISBN: (纸本)9798350379860;9798350379877

The growth stage significantly influences both the yield and quality of Panax notoginseng. Accurate plant phenotypic parameters are crucial for the precise management of P. notoginseng cultivation. Currently, traditional methods like manual measurements and tools are used for collecting phenotypic information, but they are inefficient and costly. Manual measurements are prone to subjective biases and can potentially harm plants irreversibly. While 2D images can capture plant phenotypic information, they often suffer from incomplete data and lack precision, especially for plants with complex structures like P. notoginseng. This limitation makes it challenging to achieve comprehensive and accurate measurements. To address these challenges, this study proposes a novel approach using a Neural Radiance Field (NeRF) for extracting phenotypic parameters from P. notoginseng. By capturing video and multi-view image sequences of P. notoginseng, we were able to achieve high-fidelity 3D rendering of the plants and extract point cloud data from them. This approach enabled accurate measurement of plant height and leaf area parameters. The results demonstrate promising accuracy, with an average percentage error of 1.76% for plant height and 1.73% for leaf area based on the point cloud measurements obtained using NeRF. This method leverages advanced computational techniques to overcome the limitations of traditional 2D imaging methods, offering a more comprehensive and precise means of phenotypic characterization for complex plant structures like P. notoginseng.

关键词： component Panax notoginseng image Sequence Neural Radiation Field 3D reconstruction plant phenotype

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Defending against Adversarial Attacks using Digital image Processing 2

Defending against Adversarial Attacks using Digital Image Pr...

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2023 2nd International conference on Machine Vision, Automatic Identification and Detection, MVAID 2023

作者： Xiao, Yuyang Deng, Xiaoyan Yu, Zhuliang College of Automation Science and Engineering South China University of Technology Guangzhou China

The rapidly maturing deep neural networks are used in self-driving cars, facial recognition payment, security, and other aspects, and are crucial to how we conduct our daily lives. However, past research has shown that adversarial attacks pose a significant danger to deep learning. This paper proposes a defense based on digital image processing to protect the handwritten digit recognition model from adversarial attacks. This method can defend against adversarial attacks simply and effectively using spatial filtering and thresholding for image pre-processing to remove adversarial perturbations. The experimental findings on the MNIST data set show that this technique can increase the average accuracy of the model against adversarial samples to 91.20%. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Multimedia IoT data Compression using Deep Learning Technique 2

Multimedia IoT Data Compression using Deep Learning Techniqu...

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2nd International conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Yathavaraj, V. Karthikeyan, N. Kousalya, A. Sindhuja, P. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India United Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Due to the considerable complexity of the multimedia data acquired, IoT (Internet of Things) and multimedia will encounter a number of energy and communication overload limits. One well-known solution to the problem of large datasets is to use the lossy compression algorithm. However, the lower transmission rate required by current lossy compression techniques to ensure endurable apparent image/picture quality only results in a minor reduction in the amount of communicated data and, as a result, a minor reduction in power and bandwidth use. An effective firmness technique is required to strike a fair equilibrium flanked by information volume and visual degradation. A deep-learning super-resolution technique is utilized in this study to restore high-resolution photographs from damaged photos with a considerable compression ratio as input. The results of the study clearly show how the suggested approach improves the graphical fidelity of compressed and down scaled images. The suggested method reduces largely the communication transparency and energy consumption of constrained multimedia IoT framework components as a result. © 2023 IEEE.

关键词： Internet of things

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Brain Tumor MRI image Segmentation and Classification based on Deep Learning Techniques 2

Brain Tumor MRI Image Segmentation and Classification based ...

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2nd International conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Arafat, Ali Mamtani, Dipesh Jansi, K.R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350348002

Detection and diagnosis of brain tumors is important for improving the possibility of successful treatment and recovering. Magnetic resonance imaging (MRI) is widely used imaging method for treating and recovering brain tumors. However, manual identification of brain tumors from a large amount of MRI images is time-consuming and requires specialized expertise. To overcome these challenges, computer- assisted intelligent systems are increasingly being used to speed up the medical assessment as well as treatment recommendations. The aim of our research is for coming up with a deep learning system that can segment and classify tumors in brain. The U-Net model is used for segmentation of the MRI images, while Convolution Neural Network (CNN) is used for the classifying brain tumors. Performance metrics such as accuracy, precision and recall are used to evaluate the effectiveness of this approach. The suggested CNN classifier has given the accuracy of nearly 98% for both the training and validation data. By using deep learning techniques, the following system attempts to provide accurate and effective segmenting and classifying tumors. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Classification of Skin Lesion Using image Processing and ResNet50 2nd

Classification of Skin Lesion Using Image Processing and Res...

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2nd International conference on Big data, Machine Learning, and Applications, BigDML 2021

作者： Pradhan, Adarsh Saha, Subhojit Das, Abhinay Barman, Santanu Department of Computer Science and Engineering Girijananda Chowdhury Institute of Management and Technology Azara Guwahati India

ISBN: (纸本)9789819934805

One of the most critical concerns of healthcare is cancer and the prevalence of skin cancer in the population has been increasing gradually and ominously. In this paper, we have proposed a deep learning model using a pre-trained neural network architecture called ResNet-50, and classify the dermoscopic images collected from the International Skin Imaging Collaboration (ISIC) archive into either benign or malignant. Our objective here is to make the medical diagnosis of skin cancer much more cost-effective and as efficient as possible. First, the input images have to undergo image preprocessing to remove unwanted noise, then we perform image augmentation to increase the number of images. Next, by implementing Otsu’s method for thresholding coupled with masking using bitwise And operation, we segment the lesion portion of the image. Our model acquired satisfying results with an accuracy of about 85% and the AUC (Area Under The Curve)–ROC (Receiver Operating Characteristics) value of about 0.852. The precision, recall and F1-score values for benign class stand at around 0.89, 0.82 and 0.85 respectively, whereas, for malignant class, it is about 0.80, 0.88 and 0.84 respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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data-centric AI practice in maritime: securing trusted data quality via a computer vision-based framework 2

Data-centric AI practice in maritime: securing trusted data ...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Wang, Ke Tristan, Ong Qi Hao Zhang, Xiaocai Fu, Xiuju Qin, Zheng ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way16-16 Connexis Singapore 138632 Singapore Singapore Polytech Sch Comp 500 Dover Rd Singapore 139651 Singapore

ISBN: (纸本)9798350354102;9798350354096

The advancement of data-driven Artificial Intelligence (AI) applications becomes increasingly important in optimizing maritime operations. Establishing trustworthy decisionmaking processes hinges upon precise diagnosis of data quality-a fundamental prerequisite. However, prevalent statisticalbased methodologies encounter inherent challenges, such as requiring precise threshold settings, overlooking contemporary insights from recent data, etc. To address these challenges, this study proposes a vision-inspired framework for the classification of Automatic Identification System (AIS) data quality issues, particularly within highly imbalanced datasets. Four typical data quality issues are included in this study, namely Zig-zag Value, Identity Theft, Temporal Missing, and Abnormal Constant Value. The overall framework includes a graphical transformation process to represent the spatial information of the trajectories, a data augmentation process to mitigate the class imbalance issue, and a deep learning model for image classification. Extensive experiments show that 1)The proposed method could achieve an impressive 99.29% accuracy and 99.27% F1 score in AIS data quality issue classification;2)The ConvNeXtV2 model, an enhanced convolutional neural network, demonstrated its superiority in this application, overtaking other state-of-the-art models by 2.14% in accuracy, 2.35% in F1 score, and 3.40% in MCC;3) The MixUp-based data augmentation method outperformed other imbalance learning strategies such as CutOut, Focal Loss, WeightedLoss, etc. As one of the first few practices on datacentric AI in the maritime sector, this study promises to notably reinforce maritime data reliability, fostering enhanced decisionmaking processes industry-wide.

关键词： data-centric AI image classification AIS data quality imbalance learning data augmentation

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