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Skin Cancer Detection Using Deep Learning Approach 7th

Skin Cancer Detection Using Deep Learning Approach

引用

7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ankush Singh, Samridhi Singh, Nagendra Pratap Rathee, Priyanka Department of Computer Science and Engineering National Institute of Technology HP Hamirpur India

ISBN: (纸本)9789819908370

Skin cancer is a severe health issue. Thus, the major concern of physicians is to investigate a precise clinical diagnosis. At present, some mechanisms are developed in the area of image processing with the help of algorithms and systems to detect and classify skin cancer. computer-based technology provides a relaxed, inexpensive and quick diagnosis of skin cancer symptoms. Several techniques, non-invasive in nature, have been proposed to investigate the symptoms of skin cancer, whether they represent melanoma or non-melanoma. The general process applied in skin cancer detection is image acquisition, pre-processing, segmentation of the acquired pre-processed image, extraction of the required features and classification of disease skin cancer. After implementing the model we get an accuracy of 88.48% in the case of AlexNet and 90.41% in the case of VGG 16 respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image acquisition

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Automated Diagnosis of Lung and Colorectal Pathologies Using a Shallow Capsule Network Model 2

Automated Diagnosis of Lung and Colorectal Pathologies Using...

引用

2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Vishwakarma, Harsh Vani, M. National Institute of Technology Department of Computer Science and Engineering Karnataka Surathkal India

ISBN: (纸本)9798350314168

One of the most hazardous diseases is cancer caused by many biochemical abnormalities. Among all the cancers, lung and colon cancer are the most common and tragic diseases. Effective detection of thoracic and colorectal pathology is vital for timely diagnosis and treatment. In this study, we propose a shallow capsule network model for detecting malignancies in lung and colorectal imaging. Our proposed model is trained and tested on three different datasets: LC25000, IQ-OTHNCCD Lung Cancer Dataset and Chest CT-Scan Dataset with varying train and test data ratios. Despite its shallow architecture, the proposed model achieves high accuracy, with test accuracy metrics of 99.32%, 89.05%, and 99.09% on the respective datasets. We have also shown that the proposed capsule network outperforms traditional deep learning models using less training data. Our findings show that the suggested shallow capsule network model is effective in identifying lung and colorectal disease. © 2023 IEEE.

关键词： Pathology

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Intelligent DDoS Attack Detection for IoT Network Scenario using NS3

Intelligent DDoS Attack Detection for IoT Network Scenario u...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Chaudhari, Shilpa Shashikant Manjula, L. Muttur, Chandana S. Shadaksharaiah, Anushree Maligehalli Preethi, D. Sparshika, T.N. Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350315455

IoT technology and IoT smart devices are being used widely in recent years. As the devices and services are being developed with more sophisticated technologies, their security is a prime factor to rely upon, in order to keep the services up and running properly. Among IoT network cyber attacks, DDoS attacks are prevalent. In order to detect and mitigate these cyber attacks, many machine learning based solutions are being implemented considering the kaggle datasets. this paper focus to detect DDoS attacks in IoT networks by using the proposed hybrid model of Jellyfish Swarm Optimization (JSO) and random forest (RF) algorithms using NS3. The DDoS scenario is designed and simulated in NS3 to generate the IoT DDoS dataset. JSO is bio-inspired algorithm used for feature selection while RF is machine learning used to detect DDoS. The proposed hybrid model improves the accuracy by 2 % compared with RF. Precision, Recall, f1-score statistics are also improved. © 2023 IEEE.

关键词： Bio-inspired model DDos attack IoT devices NS3

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Urban Change Detection based Convolutional Auto Encoder - Content Loss using High-Resolution Images

Urban Change Detection based Convolutional Auto Encoder - Co...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Geethaprathibha, K. Alabdeli, Haider Mamatha Bai, B.G. Kumar, Bura Vijay Divya, Boya Department of It Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

The Change Detection (CD) in high-resolution images is significant for understanding the surface of land. There have developed numerous Deep Learning (DL) approaches to CD, many of these algorithms failed to predict edges and protect changed area of shape from the multi-spectral images. In this manuscript, the Convolutional Auto encoder - Content Loss (CAE-CL) approach is developed to detect the changes in urban areas. The CL function is used in the CAE, which enhances the performance of CAE to detect the changes of urban areas. Initially, the deep features are extracted by using the initial layers of CAE and then the features are given to final detection layer. This last layer detects the changes in urban areas effectively with high performance. Various evaluation measures considered to evaluate the performance of CAE-CL approach. The CAE-CL technique reached out the accuracy of 99.51%, precision of 99.75%, f1-score of 98.92%, Intersection of Union (IoU) of 98.03% and Kappa Coefficient (KC) of 89.27% while comparing with existing techniques. © 2024 IEEE.

关键词： Change detection

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DEEP LEARNING BASED LAND COVER ASSESSMENT USING HIGH RESOLUTION SATELLITE DATA 8

DEEP LEARNING BASED LAND COVER ASSESSMENT USING HIGH RESOLUT...

引用

8th International Conference on Computing in engineering and technology, ICCET 2023

作者： Srivastava, Shivang Sharma, Yashasvi Prakash, Aditya Gupta, Shruti Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781839539237

Satellite imagery analysis is crucial for various applications such as environmental monitoring, land-use management, and disaster management. However, traditional image processing methods often struggle with accurately classifying different land use types due to variations in texture, shape, and pattern. This research work proposes a deep learning-based approach for land use classification in satellite imagery data. In this work, a Convolutional Neural Network (CNN) model has been trained on a large dataset of satellite images and corresponding land use labels, achieving high performance in land use classification as measured by metrics such as Jaccard index and accuracy from the dice function and focal function. The proposed approach offers several advantages as compared to the tradition ones. This trained model can recognize subtle differences in land use types that might be challenging to discern using traditional methods. The model is highly efficient, capable of processing large volumes of data in a relatively short time. It can be easily adapted to different satellite imagery datasets and land use classification tasks, making it a flexible and versatile approach. With the proposed methodology on analysing images against 6 classes we get a land cover classification accuracy of 81.47%. © The Institution of engineering & technology 2023.

关键词： Neural network models

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A Deep Learning Approach for Gaussian Noise-Level Quantification

A Deep Learning Approach for Gaussian Noise-Level Quantifica...

引用

International Conference on Robotics, Control, and computer Vision, ICRCCV 2022

作者： Yadav, Rajni Kant Singh, Maheep Kumain, Sandeep Chand Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789819902354

Image noise and its reduction have been of active interest over the last few years due to the increasing application of Image processing in various domains such as medical, video processing, robotics, etc. This paper presents a Deep Learning (Convolutional Neural Network) approach to classify an image into different levels of Gaussian noise it has. Most of the image-denoising models are based on the assumption that the images to be processed have some level of noise in them. The proposed model evaluates whether the image has noise present in it and also quantifies the amount of noise the image has. The MSRA 10K dataset has been used to train, test, and validate the model. Ten levels of Gaussian noise have been introduced to the dataset which represent the ten classes for the model and one class with a clean image. The model has achieved an accuracy of 96%. The proposed model has proven to be very effective in identifying the level of Gaussian Noise present in the image. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image denoising

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XGB-RFE: An XGBoost Approach for Improved Playback Spoofing Detection in Automatic Speaker Verification Systems Using Recursive Feature Elimination 11

XGB-RFE: An XGBoost Approach for Improved Playback Spoofing ...

引用

11th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2023

作者： Sanghvi, Harshil Mankad, Sapan H. Institute of Technology Nirma University Ahmedabad Department of Computer Science and Engineering India

ISBN: (纸本)9798350326147

Playback spoofing, the act of impersonating another speaker by playing back recordings of their voice, poses a significant threat tot he security o f Automatic Speaker Veri-fication (ASV) systems. This paper introduces anew method for detecting playback spoofing in A SV systems. O ur approach combines temporal and spectral features with Machine Learning (ML) and Deep Learning (DL) techniques to distinguish between playback spoofed and genuine speech. We employed Recursive Feature Elimination (RFE) to retain the most relevant features, improving performance. The results of our evaluation on a subset of ASV spoof 2019 dataset show that the combination of RFE and XGBoost produced outstanding results, with accuracy rising from 89.07% to 99.86% and Equal Error Rate (EER) decreasing from 48.05% to a mere 0.69%. Our study highlights the significance of RFE in enhancing the performance of speaker verification systems and the potential of XGBoost as an algorithm for detecting playback spoofing attacks. © 2023 IEEE.

关键词： Deep learning

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Wave Deep Neural Network for Multi-Class MRI Brain Image Classification 2

Wave Deep Neural Network for Multi-Class MRI Brain Image Cla...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Ramasamy, Viswanathan Vinodhini, V. Saravanan, T.R. Kumar, S. Dinesh Lavanya, S. Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350347579

The human brain is considered to be the most important organ in the body. Since the causes of brain cancer is still unknown, early detection is required for proper treatment. Magnetic Resonance Imaging (MRI) is an imaging technology used to depict inside structure of human body in details. This research study makes a contribution to the development of an image-based classification system as well as to the detection of brain cancer. Texture-based energy descriptors are retrieved using Discrete Wavelet Transform (DWT)-based sparse representation systems. A Convolutional Neural Network (CNN) in wavelet domain is used for further processing in order to improve the classification between normal and abnormal classes of MRI brain images. The performance of the proposed system is assessed by utilizing the standard collection of brain tumor images available in the REMBRANDT database. Results prove that the proposed system provides 98% accurate results for brain cancer classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Investigation of symmetric and asymmetric eye patterns for bell's palsy diagnosis 21

Investigation of symmetric and asymmetric eye patterns for b...

引用

21st OITS International Conference on Information technology, OCIT 2023

作者： Prasad, Thati Ravi Naulegari, Janardhan Visvesvaraya National Institute of Technology Department of Computer Science and Engineering Nagpur India GITAM Deemed-To-be-University GITAM School of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350358230

The development of an objective tool for the diagnosis of Bell's palsy (BP) has garnered significant attention among researchers, as it can aid in early treatment. This study aimed to address three research questions in the context of classification of individuals with BP or non-BP conditions. First, whether symmetric and asymmetric parameters in eye features can be reliable indicators for classification of BP and non-BP individuals. The results showed that classifiers use these parameters for classification. Second, To investigate whether the integration of both symmetric and asymmetric patterns of eye features can improve the performance metrics of classification. The findings supported that combination gives better performance. Third, how the incorporation of machine learning classifiers utilizing symmetric and asymmetric eye patterns contributes to the development of an automated classification model for distinguishing BP and non-BP subjects. The findings indicate that the Support Vector Machine(SVM) classifier achieved the highest accuracy of 92% when using the combination of symmetric and asymmetric eye parameters. This supports that the SVM classifier in leveraging the complementary information offered by both patterns to accurately classifying the individuals with BP and non-BP conditions. Overall, this study highlights the potential of symmetric and asymmetric eye patterns as reliable indicators for distinguishing between BP patients and non-BP individuals. © 2023 IEEE.

关键词： Classification (of information)

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Hand Detection and Morse Code Translation for Alternative Communication

Hand Detection and Morse Code Translation for Alternative Co...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Seshadri, Prithvi Dananjayan, Sathian Ahmed, Hasmath Farhana Thariq Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Malaysia

ISBN: (纸本)9798350315127

This paper focuses on developing a real-time Morse code translation system using finger movements captured by a camera and processed with the MediaPipe framework. To enable this, the paper compares the performance of three hand detection algorithms: Yolov5, RetinaNet, and Detectron 2, and identifies MediaPipe as the most effective method for hand tracking. The proposed approach provides a practical and accessible communication method for individuals with speech disorders and opens up new opportunities for improved communication in various settings. The novelty of this work lies in the combination of hand detection algorithms and Morse code translation to develop a robust and efficient communication system for those with speech disorders. Overall, this study highlights the potential of Assistive Speech Technologies in improving communication and promoting inclusion for individuals with speech disorders. © 2023 IEEE.

关键词： Palmprint recognition

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