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Using image Classification-based RBC Extraction for Malarial Parasite Identification in Peripheral Blood Smear 2

Using Image Classification-based RBC Extraction for Malarial...

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2nd International conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Kundu, Tamal Kumar Anguraj, Dinesh Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vijayawada India

ISBN: (纸本)9781665491990

Malaria is a dangerous, irresistible, and perilous mosquito-borne blood disease brought about by Plasmodium parasites. The traditional and most standard approach to diagnosing malaria is by outwardly analyzing blood smears through conventional microscope for parasite-contaminated red platelets under the conventional microscope by qualified specialists. This technique consumes more time and the conclusion relies upon the experience and the information provided by the individual performing the assessment. A red platelet is viewed as contaminated if something like one parasite can be identified inside its inside. White platelets and free-drifting parasites are not thought of. The present status of the craftsmanship includes manual counting by a research facility expert or other person, who can recognize staining antiquities from actual nuclei, white platelets, and (contingent upon explicit prerequisites) life cycle and types of malarial parasites. In any case, the commonsense presentation has not been sufficient up until this point. This gives us all the inspiration to make malaria location and finding quick, simple, and proficient. The main aim of this study is to assemble a model to recognize cells from images of various cells in slender blood smear on standard magnifying lens slides and characterize them as either contaminated or uninfected with right on time and compelling testing utilizing image processing. This image processing method focusing on automated screening of malarial parasite contamination in microscopic image of thin blood stain. © 2023 IEEE.

关键词： Platelets

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Conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and image Processing in Smart Firearms 2nd

Conservation of Wildlife in Hunting Tourism Using Artificial...

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2nd International conference on Information Technologies and Their Applications

作者： Asil, Ufuk Nasibov, Efendi Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced image processing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.

关键词： Artificial Intelligence image Processing Smart Firearms Wildlife Conservation Hunting Tourism

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A Robust Breast Cancer Classification Model using Extra-Trees Classifier for Histopathological image 2

A Robust Breast Cancer Classification Model using Extra-Tree...

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2nd IEEE International conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sajiv, G. Ramkumar, G. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350315905

Thousands of people die every quarter from breast cancer. Diagnosis and treatment at an early stage can drastically lower mortality rates. Traditional manual diagnosis, on the other hand, necessitates a large amount of labor by pathologists and is prone to diagnostic mistakes the longer they work. Rapid and accurate diagnosis are greatly aided by automatic histopathological image recognition. The biomedical industry has been drawn to Artificial Intelligence and its innovative methodologies because of its familiarity with the field's successes. Recent research has shown that AI can grasp details better than humans, leading to more accurate findings that aid professionals in making more informed judgments. This study presents the Extra-Tree classifier (ETC) for breast cancer image categorization. These findings demonstrate that ETC outperformed the other algorithms we examined for this data in terms of accuracy. Future researchers in the field of breast cancer will be able to use the findings of this study to guide their investigations and inform their efforts to boost the efficiency of certain algorithms. © 2023 IEEE.

关键词： Diseases

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Network Model to Generate Visual Realism images from Textual data 2

Network Model to Generate Visual Realism images from Textual...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Vidyullatha, Pellakuri Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Shaik, Amjan Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of CSE Koneru Lakshmaiah Educationn Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering St.Peters Engineering College TS Maisammaguda India

ISBN: (纸本)9798350383867

Generative Adversarial Networks (GANs) have the ability to produce realistic images from random noise vectors and has attracted a lot of attention in recent years. The textual input is discontinuous and requires efficient semantic information capture, extending GANs to create images from text descriptions has special obstacles. In this research, a unique GAN-based text-to-image creation method that makes use of conditional GAN architectures and text embeddings. The generator neural network in our suggested model converts text embeddings into appropriate image representations, whereas the discriminator network separates real images from those produced by the generator. This paper shows the strategies like multi-modal fusion mechanisms and attention mechanisms in the network architecture to help textual descriptions and visual information match. This method uses the benchmark datasets and show that it can produce realistic and varied visuals that correspond with textual descriptions. Both qualitative and quantitative assessments demonstrate our model's capacity to grasp minute details and subtle semantic nuances found in the input text. In addition, we perform user studies to evaluate the fidelity and perceived quality of the generated images relative to ground truth images. Based on our experimental findings, it appears that the suggested framework for text-to-pixel image production provides both textual relevance and image quality. This paper illuminates how our methodology may be utilized in different settings, including virtual universes, PC supported plan, and the production of imaginative substance. We likewise accentuate future bearings and potential for profound learning and normal language handling procedures to further develop text- to-picture creating models' abilities. © 2024 IEEE.

关键词： Generative adversarial networks

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Application of Convolutional Neural Networks for Cervical Cancer Detection in Women's Uterus 2

Application of Convolutional Neural Networks for Cervical Ca...

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2nd International conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Ramesh, Janjhyam Venkata Naga Visalakshi, J. Kishore, Ganjikuntla Uday Samuel, John Neerugatti, Vikram Yashika, S. Graphic Era Hill University Department of CSE Uttarakhand India CMR Institute of Technology Dept. of CSE -AIML Telngana India CMR College of Engineering & Technology Dept. of CSE-Data Science Telngana India MLR Institute of Technology Department of CSE Telngana India Dept. of CSE Banglore India

ISBN: (纸本)9798350367461

HPV infection is frequently the cause of cervical cancer, the fourth most prevalent type of cancer that mostly affects women's cervixes. Early vaccination of women may be the key to its eradication, but this effort is fraught with difficulties, particularly in middle-class or lower-income nations where access to healthcare is scarce. In this paper, a novel method for detecting cervical cancer using medical image processing techniques and Convolutional Neural Networks (CNNs) is presented. A serious health risk is cervical cancer, especially in areas with poor access to medical facilities. By using CNNs to analyze medical pictures, such as pap smears and cervical scans, the suggested approach seeks to overcome this difficulty by enabling the early identification of abnormalities in the cervical region that may be signs of cancer or precancerous lesions. data collection, preprocessing, segmentation, feature extraction, model training, and evaluation are all steps in the workflow. The CNN model shows promising performance in reliably diagnosing cervical cancer by automatic feature extraction from labelled datasets. The efficacy of the suggested strategy is confirmed by evaluation measures including area under the ROC curve, sensitivity, specificity, and accuracy. © 2024 IEEE.

关键词： Cervical cancer convolutional neural network diagnosis medical image processing pap smear

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Video Denoising using Temporal Coherency of Video Frames and Sparse Representation 12

Video Denoising using Temporal Coherency of Video Frames and...

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12th Iranian/2nd International conference on Machine Vision and image Processing, MVIP 2022

作者： Torkashvand, Azadeh Behrad, Alireza Shahed University Electrical Engineering Department Tehran Iran

ISBN: (纸本)9781665412162

Sparse representation based on dictionary learning has been widely used in many applications over the past decade. In this article, a new method is proposed for removing noise from video images using sparse representation and a trained dictionary. To enhance the noise removal capability, the proposed method is combined with a block matching algorithm to take the advantage of the temporal dependency of video images and increase the quality of the output images. The simulations performed on different test data show the appropriate response of the proposed algorithm in terms of video image output quality. © 2022 IEEE.

关键词： image reconstruction

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Privacy-preserving Federated Learning for Industrial Defect Detection Systems via Differential Privacy and image Obfuscation 2

Privacy-preserving Federated Learning for Industrial Defect ...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Lin, Chia-Yu Yeh, Yu-Chen Lu, Makena Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Yuan Ze Univ Dept Comp Sci & Engn Taoyuan Taiwan

ISBN: (纸本)9798350354102;9798350354096

Artificial Intelligence (AI) has been widely used in manufacturing to detect defects. AI models utilize product images to distinguish whether a product is normal or abnormal. If attackers use the model inversion attack to attack AI models, the input images can be roughly restored, resulting in product information leakage. In this paper, we propose a Privacy-preserving Industrial Defect Detection System (PIDS), which includes three image obfuscation methods to hide input image information and uses them to train the model. Federated learning and differential privacy are also applied to ensure that sensitive data remains decentralized and secure, even during training. Federated learning allows the model to be trained across multiple local datasets without centralized data collection, thereby reducing the risk of data exposure. Differential privacy adds another layer of protection by adding randomness to the learning process, making it hard for attackers to extract sensitive information from the trained model. Experiments show that the proposed system can achieve a high accuracy level of 96.5% in defect image classification. Therefore, the proposed system can detect defects accurately and preserve product information in terms of data and models.

关键词： Industrial defect detection federated learning differential privacy image obfuscation

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A Deep Learning based Brain Tumour Detection using Multimodal MRI images 2

A Deep Learning based Brain Tumour Detection using Multimoda...

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2nd International conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Jansi, R. Kowsalya, S. Seetha, S. Yogadharshini, A. Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur603203 India

ISBN: (纸本)9798350340235

Unchecked and fast cell proliferation leads to brain tumors. Death could result if the condition is not treated in the beginning stages. The variety of tumor location, shape, and size poses a significant obstacle to the detection of brain tumors. Through image preprocessing, data augmentation, and convolutional neural networks (CNNs), this research gives a thorough method for detecting brain tumors. This work utilizes multimodal MRI images from brain tumor Brats dataset, which is improved through preprocessing, dilation and erosion. Dilation and erosion produces a better view of the tumor regions for accurate detection. A CNN model is then trained with a focus on shuffling the data for better performance. TensorFlow and Keras libraries were utilized for the implementation of the proposed system. The proposed framework achieved a good accuracy of 98.2% in detection of brain tumours using the brain tumor Brats dataset. This system can assist the health care professionals to accurately predict the presence of brain tumors. © 2023 IEEE.

关键词： Brain tumor detection Convolutional Neural Networks data augmentation Deep learning image preprocessing

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Hybrid CNN-RNN Model for Accurate image Captioning with Age and Gender Detection 2

Hybrid CNN-RNN Model for Accurate Image Captioning with Age ...

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2nd International conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Jain, Bhavik Doshi, Khushi Dwivedi, Pulkit Chandigarh University Chandigarh India

ISBN: (纸本)9798350340235

Age and gender detection is a computer vision process that aims to identify the age and gender of individuals based on their physical characteristics, primarily facial features. This research paper presents a novel approach to age and gender detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our proposed method combines CNN and RNN models to accurately detect age and gender from input images. To address the limitations of existing datasets and the scarcity of data, we have developed a unique dataset that includes diverse facial images annotated with age and gender information. Our approach leverages CNN to extract region-based visual features from input images, while the RNN-based model decodes output captions word by word. Attention mechanisms are incorporated to highlight relevant regions in the input image during the detection process. Our results demonstrate high accuracy rates for both age and gender detection, showcasing the potential of the proposed method in various domains such as security, healthcare, and entertainment. This study offers a promising solution to the age and gender detection challenge, underscoring the effectiveness of CNN and RNN models in image analysis. © 2023 IEEE.

关键词： Convolutional neural network(CNN) Deep learning Long short-term memory(LSTM) Natural language processing(CNN) Recurrent neural network(RNN) VGG-16

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Scale Space Mining Algorithm and Application Analysis Based on Computer image Processing 2

Scale Space Mining Algorithm and Application Analysis Based ...

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2nd IEEE International conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Fu, Rong City Institute Dalian University of Technology Dalian China

ISBN: (纸本)9781665493963

In the process of computer IP(CIP), computer vision is used as the basis of image processing(IP). images contain a lot of information, which is the basis for people to analyze image data. However, because images contain noise that affects the analysis results, intelligent extraction of image information has become the key to computer vision analysis of image data. According to the SS theory, this paper proposes three CIP algorithms, such as the EM image segmentation algorithm based on one-dimensional and multi-dimensional feature scale space(SS) and the IP algorithm based on adaptive multi-scale wavelet transform(WT). Compared with the other two algorithms, the three algorithms obtained the MSE value, PSNR value and SSIM value of the Q1 image. With the increase of the denoising level, the WT IP algorithm has the best denoising effect on the image, and with the increase of the denoising level. With the increase of sampling rate, the algorithm also has the best effect on image reconstruction. © 2022 IEEE.

关键词： Wavelet transforms

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