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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images

Procedia computer science 2025年 252卷 108-117页

作者： Umme Sara Utpol Kanti Das Juel Sikder Sudipta Roy Chowdhury Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar-4500 Rangamati Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%.

关键词： Skin Lesion Cancer-CNN (SLC-CNN) Skin Cancer Deep Learning Classification Segmentation

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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A Review on Bioinformatics using DNA Cryptography 8

A Review on Bioinformatics using DNA Cryptography

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kairi, Animesh Bhadra, Tapas Karmakar, Samit Verma, Aman Saha, Sonia Gupta, Soumili Chowdhury, Souraja Aliah University Computer Science Kolkata India University of Engineering & Management Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798331520762

DNA-based encryption with bioinformatics has emerged as a promising field for secure data storage and transfer. This study analyzes the bioinformatics aspects of DNA cryptography, focusing on important advancements and challenges. We look at techniques like DNA origami, enzymatic reactions, and DNA strand displacement that use DNA sequences to encode and decode data. Additionally discussed are the security implications of DNA-based cryptography, including privacy issues, data integrity issues, and the potential for DNA-based attacks. Finally, we outline some possible directions for further review, such as developing more robust error-correction systems, looking at cutting-edge DNA-based encryption methods using bioinformatics, and addressing the ethical and societal implications of this emerging technology. © 2025 IEEE.

关键词： Bioinformatics

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm

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Procedia computer science 2025年 252卷 510-518页

作者： Priya M Vijaya kumar K Vennila P Prasanna M A Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Lecturer Dept. of Computer Science and Engineering Women’s Engineering College Puducherry- 605008 Asst. Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Asst. Professor Dept. of Computer Science and Engineering K.Ramakrishnan college of Technology Trichy -621112

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one’s own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers’ opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner).

关键词： Data analytics Sarcasm Lexical dictionary N-gram Deep learning Text mining

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Adaptive Testing for LLM-Based Applications: A Diversity-Based Approach 18

Adaptive Testing for LLM-Based Applications: A Diversity-Bas...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Yoon, Juyeon Feldt, Robert Yoo, Shin KAIST School of Computing Daejeon Korea Republic of Chalmers University Dept. of Computer Science & Engineering Gothenburg Sweden

ISBN: (纸本)9798331534677

The recent surge of building software systems powered by Large Language Models (LLMs) has led to the development of various testing frameworks, primarily focused on treating prompt templates as the unit of testing. Despite the significant costs associated with test input execution and output assessment, the curation of optimized test suites is yet overlooked in these tools, which calls for tailored test selection or prioritization strategies. In this paper, we show that diversity-based testing techniques, such as Adaptive Random Testing (ART) with appropriate string distance metrics, can be effectively applied to the testing of prompt templates. Our proposed adaptive testing approach adjusts the conventional ART process to this context by selecting new test inputs based on scores derived from existing test suite and their labelling results. Our results, obtained using various implementations that explore several string-based distances, confirm that our approach enables the discovery of failures with reduced testing budgets and promotes the generation of more varied outputs. © 2025 IEEE.

关键词： Software testing

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Analysis of optimization Techniques for Dynamic Neural Networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 IEEE.

关键词： Neural network models

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Speech-Guided Drone Control System Based on Large Language Model

Speech-Guided Drone Control System Based on Large Language M...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Choi, Seok-Hun Kim, Zeen-Chul Buu, Seok-Jun Gyeongsang National University Dept. of Computer Science and Engineering Jinju-si Korea Republic of Aerospace Experts Corporation Daejeon Korea Republic of

ISBN: (纸本)9798331510756

The growing use of drones in logistics, agriculture, surveillance, and search-and-rescue operations has highlighted the need for more intuitive and adaptable control systems. Traditional control methods, such as joysticks and mobile applications, often necessitate technical expertise, thereby limiting their accessibility in hands-free or time-sensitive scenarios. This paper introduces a Speech-guided Drone Control System that utilizes Large Language Model (LLM) to process and execute natural language commands. The system is composed of three key components: speech-to-text conversion, natural language command interpretation, and drone control execution. The proposed system enables users to issue complex commands through natural speech, which are then processed and broken down into executable drone actions in real time. The system underwent rigorous testing across various levels of command complexity, demonstrating high accuracy and responsiveness even in handling ambiguous or incomplete commands. The results suggest that this approach not only simplifies drone operation but also represents a transformative step toward making advanced drone control universally accessible and highly efficient in critical sectors such as disaster response and precision agriculture. © 2025 IEEE.

关键词： Drones

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Reviewing the Role of Blockchain Enhanced Smart Contract Security Framework in Healthcare 3

Reviewing the Role of Blockchain Enhanced Smart Contract Sec...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Gupta, Swati Misra, Dinesh Chandra Dr. K.N Modi University Dept. of Computer Science and Engineering Rajasthan Newai India

ISBN: (纸本)9798331533205

Blockchain technology, implemented via smart contracts, provides significant improvements in data security, transparency, and automation within healthcare applications. Smart contracts are inherently unsuitable for critical healthcare settings. This is due to vulnerabilities such as reentrancy attacks, overflow concerns, and unauthorized access to sensitive data. This study introduces the Blockchain Enhanced Smart Contract Security Framework (BESCSF) to enhance the security of smart contracts in healthcare applications. Present research is making use of hybrid security architecture. Such architecture incorporates sophisticated cryptographic methods, secure coding standards, and real-time monitoring. The suggested architecture has an adaptive hybrid approach. This approach uses machine learning to predict potential vulnerabilities. Present work prioritizes resource allocation for auditing and safeguarding smart contracts. This paradigm is capable to enhance the efficacy and precision of security measures. Present work evaluates the framework's performance, contract execution accuracy along with ability to reduce security breaches. It is performed via a series of tests and simulations. The hybrid method significantly enhances vulnerability detection accuracy by 15%. Moreover present research reduces execution time by 20% compared to traditional security solutions. Healthcare applications are considered in preset research papers that are capable to prioritizing security, efficiency, and regulatory compliance. Research work considers medical supply chain monitoring, patient data management, and insurance claims. Present work would find the method ideal. Thus, it also enhances trust and scalability. © 2025 IEEE.

关键词： Information management

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A CNN Approach to Automated Detection and Classification of Brain Tumors

A CNN Approach to Automated Detection and Classification of ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Zahid Hasan Abdullah Tamim D.M. Asadujjaman Md. Mahfujur Rahman Md. Abu Ahnaf Mollick Nosin Anjum Dristi Abdullah Al Noman Dept. of Computer Science & Engineering University of Rajshahi Bangladesh Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Khulna University of Engineering & Technology Khulna Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

For a timely diagnosis and successful treatment, brain tumors need to be carefully evaluated. Tumor inspection is complicated by morphological factors like size, location, texture, and varying appearance. Additionally, medical imaging poses challenges such as noise and incomplete data. This research article presents a methodology for processing Magnetic Resonance Imaging (MRI) data, encompassing techniques for image classification and denoising. The effective use of MRI images allows medical professionals to detect brain disorders, including tumors. This study uses MRI data analysis to classify brain tumors and healthy brain tissue. MRI is the best imaging technique for studying brain tumors because it offers a more thorough image of internal anatomical features than other imaging techniques like Computed Tomography (CT). Initially, an anisotropic diffusion filter is used to denoise the MRI picture. The models are based on an open-access, clinically validated MRI dataset for brain tumor classification, containing 3,264 brain MRI scans. SMOTE (Synthetic Minority Over-sampling Technique) is a widely used approach for tackling class imbalance in datasets by generating synthetic samples to augment and balance the data. It is utilized for dataset augmentation and balancing. Convolutional Neural Networks (CNNs), including ResNet152V2, VGG, ViT, and EfficientNet, were utilized for the classification task. Among them, EfficientNet attained an accuracy of 98%, the highest recorded.

关键词： Accuracy Anisotropic magnetoresistance Magnetic resonance imaging Biological system modeling Computational modeling Computed tomography Brain tumors Brain modeling Feature extraction Convolutional neural networks

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