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Automating skin cancer screening: a deep learning

Journal of engineering and Applied science 2025年第1期72卷 1-18页

作者： Rashad, Nada M. Abdelnapi, Noha MM. Seddik, Ahmed F. Sayedelahl, M.A. Department of Computer Science Faculty of Computers and Information Suez University Suez43221 Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo11795 Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial sources, lighter skin pigmentation, a history of sunburns, and a family history of the disease. Early detection and prompt intervention are crucial for achieving a favorable prognosis. Traditionally, treatment modalities include surgery, radiation therapy, and chemotherapy. Recent advancements in immunotherapy have revolutionized skin cancer diagnosis, but manual identification remains time-consuming. Artificial intelligence (AI) has shown potential in skin cancer classification, leading to automated screening methods. To support dermatologists, we improved the model for classifying images. This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. This study offers a novel approach to early skin cancer diagnosis based on image processing. Our approach leverages the high accuracy of a specific convolutional neural network architecture, utilizing transfer learning with pre-trained data to further enhance detection performance. Our findings demonstrate that the employed ResNet-50 transfer learning model achieves a remarkable accuracy of 97%, while ResNet50 without augmentation gives an accuracy of 81.57% and an F1-score of 75.75%. © The Author(s) 2025.

关键词： Convolutional neural networks

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A survey on augmenting knowledge graphs (KGs) with large language models (LLMs): models, evaluation metrics, benchmarks, and challenges

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Discover Artificial Intelligence 2024年第1期4卷 76页

作者： Ibrahim, Nourhan Aboulela, Samar Ibrahim, Ahmed Kashef, Rasha Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada Faculty of Engineering Alexandria University Alexandria Egypt Computer Science Western University LondonON Canada

Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains. © The Author(s) 2024.

关键词： Knowledge graph

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A new method for promoting the detection of breast cancer in thermograms: applying deep autoencoders for eliminating redundancies in parallel with preserving independent components

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 4085-4099页

作者： Shojaedini, Seyed Vahab Bahramzadeh, Bahram Biomedical Engineering Department Iranian Research Organization for Science & ampTechnology Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran

Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. Recently, deep learning has emerged as a promising framework for analyzing breast thermograms. However, the performance of deep neural networks heavily relies on the volume of training images, and the limited number of labeled thermograms necessitates the generation of synthetic images for training. This article introduces a novel method that eliminates redundant or destructive information in synthetic thermograms while preserving essential and independent features using deep autoencoder concepts. Consequently, the proposed strategy enhances the representation of synthetic images for training deep networks, leading to improved breast cancer detection in thermograms. Evaluating the performance of the proposed method on the DMR-IR dataset shows a significant improvement in detecting cancerous breasts in thermograms compared to benchmark methods. Integrating this innovation into the basic model achieved an average accuracy of 92.3%, sensitivity of 93%, and specificity of 91.4%, surpassing the basic model’s 89.1%, 86%, and 92.5%, respectively. The proposed method demonstrated superior performance in mitigating overfitting, as evidenced by a smaller gap between training and validation curves, resulting in a 7% improvement in accuracy and a 3.2% increase in sensitivity. Although there was a slight decrease of 1.1% in specificity, it was outweighed by other parameter improvements. Additionally, the method led to a significant reduction in the dispersion range of test results, approximately six times less than alternative approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lung cancer

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Towards Seamless Glucose Monitoring: A Smart Non-Invasive System for Diabetes Patients 2

Towards Seamless Glucose Monitoring: A Smart Non-Invasive Sy...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pawar, Adesh Kelzarkar, Sanket G. Choudhary, Vishal Kumar Daburkar, Ashwini Kumar, Praveen Hore, Rutuja Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Blood Glucose Monitoring levels are essential to the treatment of diabetes and must be done continuously. Patients often don't comply with conventional glucose monitoring techniques since they cause discomfort and are invasive. An Arduino microcontroller and infrared (I.R.) sensor are used in this work to provide a robust, non-invasive glucose monitoring system. I.R. light is transmitted through the skin to measure the quantity of light reflected, and this relationship exists between the amount of light and the amount of glucose in the interstitial fluid. In real-time, the Arduino analyses blood glucose levels by analyzing IR Spectroscopy and using machine learning techniques. The device features wireless connectivity, enabling it to send glucose readings to a mobile application for continuous monitoring, historical data analysis, and customized alerts. Initial trials indicate that this non-invasive system provides accurate glucose measurements comparable to traditional methods, significantly improving user comfort and compliance. This innovative approach promises a pain-free, user-friendly, and cost-effective solution for diabetes management, paving the way for more widespread adoption of continuous glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

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RBA: A Detection Method for Offensive Language in Chinese 24

RBA: A Detection Method for Offensive Language in Chinese

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2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Wang, Linya Mou, Shiyu Liu, Peize School of Computer Science and Engineering Southwest Minzu University China Engineering Faculty Biomedical Engineering Department University of Malaya Malaysia

ISBN: (纸本)9798400709876

As social media continues to gain popularity, offensive language on the Internet is on the rise, and how to effectively detect it has become a hot spot in the academic community. Due to the strong context-dependence of Chinese offensive text, the feature information is challenging to extract, and the training is susceptible to noise interference. Therefore, we propose a Chinese offensive language detection model integrating RoBERTa-base-cold, BiLSTM and adversarial training. The model generates dynamic word vectors containing offensive features by segmenting the text with RoBERTa-base-cold, then adds noise perturbations to these output vectors to generate adversarial samples and sends these adversarial samples to BiLSTM to enhance model robustness through adversarial training in the face of noise interference. Experiments are conducted on the COLDataset dataset, and the results show that this method is more effective than other modelling methods in detecting Chinese offensive speech. © 2024 ACM.

关键词： Speech recognition

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Assessing the Effectiveness of Machine Learning Algorithms in Vehicle Insurance Fraud Prediction: A Comparative Approach 3

Assessing the Effectiveness of Machine Learning Algorithms i...

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

作者： Satone, Aditya P. Daronde, Subodh Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Due to the wealth of information accessible on many insurance issues, including travel and auto insurance, individuals are becoming more and more impacted by these kinds of insurance in today's society. But this tendency also makes room for dishonest practices. Insurance fraud is harmful and unlawful, regardless of who commits it - the vendor or the customer. Fraud can come from both the customer and the vendor. The consumer may commit fraud by filing exaggerated claims or using out-of-date policies, while the vendor may market insurance from phoney firms or neglect to submit premiums. This paper presents a comparative analysis of various classification algorithms, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Linear Regression (LR), and Polynomial Regression, for detecting insurance fraud. The performance of these algorithms is evaluated using key metrics such as Precision, Recall, and F1-Score. The analysis reveals that the bestperforming model is the Random Forest, with a Mean Squared Error (MSE) of 6.50, outperforming the other techniques. © 2025 IEEE.

关键词： Random forests

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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Incident Detection with Pruned Residual Multilayer Perceptron Networks 18

Incident Detection with Pruned Residual Multilayer Perceptro...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Soubra, Mohamad Dorohinicki, Marek-Kisiel Kurdziel, Marcin Zachara, Marek Agh University Faculty of Computer Science Electronics and Telecommunications Krakow Poland Agh University Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9788396744784

Internet of things (IoT) has opened new horizons in connecting all sorts of devices to the internet. However, continuous demand for connectivity increases the cybersecurity risks, rendering IoT devices more prone to cyberattacks. At the same time, rapid advances in Deep Learning (DL)-based algorithms provide state-of-the-art results in many classification tasks, including classification of network traffic or system logs. That said, deep learning algorithms are considered computationally expensive as they require substantial processing and storage capacity. Sadly, IoT devices have limited resources, making renowned DL models hard to implement in this environment. In this paper we present a Residual Neural Network inspired DL-based Intrusion Detection System (IDS) that incorporates weight pruning to make the model more compact in size and resource consumption. Additionally, the proposed system leverages feature selection algorithms to reduce the feature-space size. The model was trained on the NSL-KDD dataset benchmark. Experimental results show that the proposed system is effective, being able to classify network traffic with an F1 score of up to 98.9% before the pruning and an F1 score of up to 97.5% after pruning 90% of network weights. © 2023 Polish Information Processing Society.

关键词： Internet of things

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Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh

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Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a Southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

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Traffic Collision Detection Using DenseNet

Traffic Collision Detection Using DenseNet

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Kaluza, Daniel Seiler, Marco Kashef, Rasha Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Canada

ISBN: (纸本)9798350323153

There has been a sharp rise in the number of fatalities and injuries caused by traffic accidents in urban areas. Cities often have video and image resources that can be analyzed manually using operators to address this problem. This paper introduces an automated collision detection system that utilizes publicly available images captured by Toronto's traffic camera system. The system is based on a Deep Learning model, specifically a DenseNet-161, employed to classify accidents and non-accidents. The results of this classification are then displayed on a graphical user interface. The primary aim of this study is to reduce medical response time and ultimately save lives by issuing automatic alerts. The proposed system has the potential to minimize the severity of accidents and decrease the number of fatalities by notifying emergency services once an accident is detected. © 2023 IEEE.

关键词： CCTV Collision detection Deep learning Internet of Things (IoT) machine learning Smart Cities traffic obfuscation

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