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Composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection 1st

Composite Awareness-Based Knowledge Distillation for Medical...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Dong, Shiwen Ding, Xiaowei Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

In the field of medical image analysis, supervised deep learning methods have become mainstream but still face significant challenges. Firstly, they require substantial annotated data, which is costly and time-consuming to obtain. Secondly, there is a class imbalance issue, especially with rare diseases that have limited images and diverse lesion types, making accurate modeling difficult. To address these issues, we propose an anomaly detection model based on knowledge distillation. Our approach leverages the differences in intermediate layer features between a pre-trained teacher network and a student network trained on normal images through knowledge distillation to detect anomalies. We incorporate a composite awareness module to learn both local regional and global spatial information effectively. Experiments conducted on three medical datasets prove our approach performs competitive anomaly detection results compared to existing methods for medical images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical image processing

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Family Doctor Model Training Based on Large Language Model Tuning 1st

Family Doctor Model Training Based on Large Language Model T...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Zhu, Yu Li, Wangtao Ling, Xufeng Yang, Jie Shanghai China School of AI Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

As society increasingly focuses on health issues, the importance of the role of family doctors becomes more apparent. The question of how to use cutting-edge technology to improve the accessibility and efficiency of medical services is a critical topic. Generative large language models, with their excellent capabilities in natural language processing, offer new possibilities for the implementation of intelligent family doctor systems. This study focuses on the application of generative large language models in family doctor systems, selecting the ChatGLM2-6B model as the base model for this research. Through a custom-written data collection program, a dataset containing 170,000 high-quality medical question-and-answer entries was constructed. The LoRA method was used to tune the base model, and the performance of the original and tuned models in medical question-and-answer tasks was examined. The practical results of this study show that the generatively tuned large language model has achieved a 70% improvement in ROUGE scores compared to the untuned model, validating the effectiveness of large language model tuning in the family doctor field. Furthermore, this study has open-sourced the collected dataset and program, providing a technical reference for deploying efficient and accurate question-and-answer systems in similar vertical fields in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Enhancing Accuracy of Handwritten Character Segmentation Using Region-Based Convolutional Neural Networks (R-CNN)

Enhancing Accuracy of Handwritten Character Segmentation Usi...

引用

AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Kanchan Varpe Sachin Sakhare Rahul Joshi Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Handwritten character segmentation plays a pivotal role in the performance of Optical Character recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance.

关键词： Training Performance evaluation Deep learning image segmentation Accuracy Quantization (signal) Text recognition Optical character recognition Writing Convolutional neural networks

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Computer Vision to Animal Footprint Classification Based on Deep learning Model 1st

Computer Vision to Animal Footprint Classification Based on ...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Rifana Fathima, A. Dhanalakshmi, K. Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031730641

An emerging area in machine vision is a real biometric system that can identify and describe animal life in images and videos these programs offer methods for classifying animals using computer vision Probabilistic Neural Network (PNN) features a well-liked deep learning technique are the foundation of the current system for classifying animal faces. Here, the suggested system analyses photos of animal footprints to categorise them using deep learning. Using a clever method, the footprint photos are pre-processed and turned into grayscale boundaries. Gabor filter are used to extract features of segmented image. The dimensionality reduction is carried out based on unsupervised model, (PCA). Convolutional Neural Network (CNN) is used for classification and identifying the animal class. Footprints 0 dataset of five different animal categories of 100 images is to be used for classification. The performance analysis of the system is evaluated using the measure accuracy, precision, recall and fl- measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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An Intelligent machine learning Framework for Melanoma Classification System: A Critique 1st

An Intelligent Machine Learning Framework for Melanoma Class...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Sridevi, S. Gowthami, S. Hemalatha, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Sri Krishna College of Engineering and Technology Coimbatore India Vellore Institute of technology Vellore India

ISBN: (纸本)9783031730672

One of the most common cancers, skin cancer accounts for roughly half of the cancer prevalence in worldwide. Melanocytes, which are cells that produce pigment, are the source of melanoma, a kind of skin cancer. Both the incidence and mortality rates of melanoma have sharply increased in recent years. The ailment that attracted a variety of studies can be treated more effectively by medical professionals with early identification. This article focuses on several segmentation techniques and a classification strategy based on machine learning for the identification of melanoma. The melanoma areas are easily recognized by the segmentation methodologies, and the classification procedure is made simpler. Convolutional neural networks (CNN) with U-Net are used for segmentation. A support vector machine (SVM) with a variety of kernels and a k-nearest neighbor (KNN) with a variety of k values are used to classify the segmented image in the classification phase. Performance demonstrates that the segmentation process significantly increases the SVM's classification effectiveness when acquired values from the machine learning classifier are compared. Further the results shall be improved by enhancing the analysis with large dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Enhanced Disease recognition and Classification in Black Gram Plant Leaves Using Deep learning 1st

Enhanced Disease Recognition and Classification in Black Gra...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Prasanth, K. Kabilamani, P. Sangar, G. Kaliraj, V. Rajasekar, V. SA Engineering College Chennai India SRMIST Vadapalani Chennai India

ISBN: (纸本)9783031730641

While urad bean is an important grain crop in several locations, it is frequently plagued by a number illness that has a devastating effect on harvest yields and quality. Using Convolutional Neural Net- works (CNNs) with 50 Layers, Local Binary pattern and Support Vector machines (SVMs), we offer a deep learning-based method for accurately recognising and categorising disorders of leaves in black gram plants. To begin, we amassed a database of pictures of black gram leaves affected by diseases like leaf blight, leaf spot, and yellow mosaic virus. We did some preliminary processing to clean up the photographs and make them look better. Then, we educated a convolutional neural network model to use to reduce the number of parameters and the LBP used to extract disease classification features from the plant images. Our model’s accuracy was enhanced by employing SVM as a classifier on the CNN50 with LBP model’s outputs features. We were able to decrease the amount of incorrect classifications by using the SVM model to boost classification precision. Using criteria like accuracy, precision, recall, and F1-score, we analysed how well our method performed. Our experiments demonstrate indicates the suggested method executes better than earlier state-of-the- art methods with a degree of 98.69%. In conclusion, we show that deep learning methods, specifically CNN and SVM, may be used to precisely detect and categorise black gram plant leaf diseases. This method may help producers better detect and combat plant illnesses, which would ultimately boost food production and quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Satellite image Classification Using Convolutional Neural Networks

Satellite Image Classification Using Convolutional Neural Ne...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE international conference on

作者： Manoj Bhaskar Chetan Barde Prakash Ranjan Nawneet Kumar Sujit Kumar Dept. of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur Bihar India Dept. of Electrical Engineering Science Technology and Technical Education Department Government Engineering College Banka Bihar Engineering University Bihar India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

image classification is crucial in remote sensing, image analysis, and pattern recognition. Object detection and classification in satellite images are crucial for applications like marine monitoring, land-use planning, ecological research, and military operations. Satellite images provide valuable spatial and temporal data, helping address real-world challenges. However, issues like data quality, availability, and distribution complicate image analysis. Deep learning (DL) algorithms have become popular for their effectiveness in classifying satellite images, sup-porting tasks such as disaster management, land-use planning, and natural resource management. This study proposed a CNN model with batch normalization for satellite image classification. The dataset includes images categorized into cloudy regions, deserts, water bodies, and green spaces. CNN excels at extracting features like edges and textures, while Batch Normalization (BN) stabilizes training by normalizing inputs and reducing covariate shifts. Together, CNN and BN improve the efficiency and accuracy of the model. The proposed model successfully classifies satellite images, outperforming existing methods with an overall accuracy of 91%, demonstrating its superior remote sensing imaae analysis capability.

关键词： Training Accuracy Biological system modeling Feature extraction Satellite images Planning Deserts Convolutional neural networks Remote sensing Batch normalization

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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IEEE students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： G.V. Thanuja Ramya S Dept. Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications.

关键词： image edge detection Computed tomography Software algorithms Signal processing algorithms Computer architecture Table lookup IP networks Logic Field programmable gate arrays Kidney

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A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans 1st

A Modified U-Net for Semantic Segmentation of Liver and Li...

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1st international conference on Computation of Artificial Intelligence and machine learning, ICCAIML 2024

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjective interpretations. Deep learning models are commonly used in this field due to their performance improvement over conventional methods of medical image processing. However, deep learning models’ capacity for class imbalance adaptation is rarely studied, particularly in the instance of segmenting liver tumours given their tiny size. Therefore, in the current study, a straightforward Convolutional Neural Network (CNN) model based on the well-known U-Net architecture is suggested in order to capture more contextual information and solve the class imbalance issue. Additionally, a combination of the loss function is intended to guarantee precise segmentation of liver tumours. A detailed qualitative and quantitative analysis is carried out on LiTs dataset to validate the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computer Aided Diagnostic Systems CT scan Deep learning Liver Tumor Semantic Segmentation

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learning-Based Outlier Rejection for Keypoint Matching

Learning-Based Outlier Rejection for Keypoint Matching

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Wissam Alkhadour Jamal Zraqou Jawad Alkhateeb E-Commerce Department University of Petra Amman Jordan CS/ Virtual & Augmented Reality Department University of Petra Amman Jordan Computer Engineering Department Prince Mohammad Bin Fahd University Alkhobar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Matching features are integral to computer vision and can be used in image fusion, object recognition, or 3D model construction. Although conventional solutions are good, such as feature detection using SIFT with FLANN or brute-force and subsequent descriptor matching, they have severe problems when applying scale, rotation, or noise transformations. Other techniques, such as RANSAC, have also been used intensively to reduce match reliability by removing outliers. Nonetheless, the authors also discussed RANSAC when it has shortcomings; they stated that RANSAC has some drawbacks, such as being sensitive to parameter values, and when used with big data, it is very computationally expensive. To overcome these challenges, we suggest that SVMs be introduced into the feature-matching process in the SIFT framework. Matches are labeled inliers or outliers using detected training examples based on the SVM's descriptor similarity and geometrical consistency. This is an efficient replacement to the conventional LOS techniques because it includes probabilistic decisions, makes complex transformations plug-in with the methodology or the measurement system employed, as well as minimizing the critical dependence on the adjustment of the parameter values. A comparison of the performance of several theories shows that SVM-based filtering yields better match reliability, higher feature coverage, and a faster processing time than existing feature-matching approaches, and hence, it complements existing feature-matching solutions appropriately.

关键词： Training Support vector machines Solid modeling Three-dimensional displays Filtering Noise Reliability theory Probabilistic logic Object recognition image fusion

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