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Enhancing Skin Cancer Detection with Multimodal Data Integration: A Combined Approach Using Images and Clinical Notes

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SN computer Science 2025年第1期6卷 1-14页

作者： Chakkarapani, V. Poornapushpakala, S. Suresh, S. School of Electrical and Electronics Sathyabama Institute of Science and Technology (Deemed to be University) Jeppiaar Nagar Rajiv Gandhi Salai Tamilnadu Chennai 600119 India Department of Computer Applications National Institute of Technology Haryana Kurukshetra 136119 India

Skin cancer is one of the most common cancers worldwide where early detection is crucial for effectively diagnosing and treatment. Traditional diagnostic methods largely depend on dermoscopic or skin lesion image analysis. However, they are limited by the clinician’s expertise and primarily rely on image data. While most of the studies focus on improving the diagnostic accuracy using the skin lesion images, this study addresses the limitations of using just the image data and proposes a novel approach that enhances skin cancer detection by combining skin lesion images with patients’ clinical notes. The methodology proposes a multi-modal approach that combines the images data with doctor’s clinical notes to enhance early diagnosis and accurate detection of skin cancer. Clinical notes are synthetically generated using two distinct multimodal large language models (LLMs), corresponding to each patient’s skin cancer images, encapsulating both visual and textual data representative of real-world scenarios. The generated clinical notes are validated through a dual-method approach involving cross-evaluation and consensus scoring, utilizing metrics such as the BLEU score, ROUGE score, Overlap Coefficient, and Jaccard index. Furthermore, this paper compares the performance of a model built using skin lesion images with four other models built using skin lesion images and synthetically generated clinical notes. The experimental results demonstrates a significant improvement in all classification metrics compared to single-modality models, achieving an accuracy of 99.51%, precision of 96.19%, recall of 97.95%and f1-score of 97.03% with the multimodal ALBEF model that uses GPT-4-turbo for clinical notes generation. The results show a significant improvement in performance with the multi modal approach for medical diagnosis. Also, this research sets a foundational framework that can be leveraged for many potential healthcare diagnoses applications. © The Author(s), under exclusi

关键词： Dermatology Generative AI Large language models Multi-modal AI Skin cancer detection Synthetic data generation

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A Comparative Analysis of Image Processing and Deep Learning Techniques for License Plate Recognition in Difficult Environments 5

A Comparative Analysis of Image Processing and Deep Learning...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tejaswi, S. Babu, Tina Tejaswi, K. Seshadripuram Degree College Department of Computer Science Mysuru India School of Engineering Department of Computer Science and Engineering Alliance University Bengaluru India JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798331518097

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 IEEE.

关键词： Intelligent systems

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A Comprehensive Review On Two-Stage Object Detection Algorithms

A Comprehensive Review On Two-Stage Object Detection Algorit...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Varghese, Rejin Sambath, M. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science School of Computing Science Chennai India

ISBN: (纸本)9798350314946

Two-stage object detection algorithms have gained significant attention in computer vision due to their robustness and accuracy in detecting objects in images. These algorithms consist of two main stages: region proposal generation and object classification/localization. The region proposal stage efficiently generates a diverse set of candidate object regions using various techniques, narrowing down the search space. In the second stage, deep convolutional neural networks extract discriminative features from the proposed regions, enabling precise object classification and localization. These algorithms have demonstrated impressive performance in complex scenes with occlusions, scale variations, and multiple objects. However, they have limitations such as slower inference speeds, increased computational complexity, and the need for additional training steps. To address these limitations, more efficient algorithms like single-stage detectors have emerged. Two-stage object detection algorithms offer a powerful framework for accurate and robust object detection, and ongoing research aims to refine and improve these algorithms, expanding the capabilities of computer vision applications. © 2023 IEEE.

关键词： Deep neural networks

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M³FuNet: An Unsupervised Multivariate Feature Fusion Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-15页

作者： Chen, Huayue Long, Haoyu Chen, Tao Song, Yingjie Chen, Huiling Zhou, Xiangbing Deng, Wu Key Laboratory of Optimization Theory and Applications at China West Normal University of Sichuan Province School of Computer Science China West Normal University Nanchong637002 China China West Normal University School of Geographical Science Nanchong637002 China Shandong Technology and Business University School of Computer Science and Technology Yantai264005 China Wenzhou University Computer Science Department Wenzhou325035 China Sichuan Tourism University School of Information and Engineering Chengdu610100 China Civil Aviation University of China School of Electronic Information and Automation Tianjin300300 China

Hyperspectral image (HSI) spectral-spatial joint feature (FE) extraction methods generally suffer from low feature retention and weak spatial-spectral dependence, which will lead to single-class feature confrontation (SCFC). To solve this problem, an unsupervised multivariate feature fusion network (M3FuNet) is developed in this article. In M3FuNet, multiscale supervector matrix correction (MSMC) and multiscale random convolution dispersion (MRCD) are used as the spectral and spatial feature extraction method, and the feature retention of spectral and spatial features is improved to achieve feature calibration by feature fusion and decision fusion, called 'multivariate feature fusion.' The MSMC is employed to correct the supervector matrix to reduce the intraclass variance in superpixel homogeneous regions and overcome the phenomenon of supervector block drift (SvBD). The MRCD uses random convolution and Gaussian smoothing to extract deep spatial features. Because of the similar feature representation ability of the MSMC and MRCD, the obtained spectral-spatial joint features have high feature retention and strong spectral-spatial dependence. Finally, this M3FuNet is used for realizing the classification of HSI. Three common HSI datasets are used to validate the effectiveness of the M3FuNet. The experiment results show that the M3FuNet has a superior performance compared with several state-of-the-art (SOTA) HSI classification methods. The code of the proposed M3FuNet is available at https://***/aichou233/M3FuNet. © 1980-2012 IEEE.

关键词： Convolution

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Temporal Feature-Based AI Models Excel Over Traditional Machine Learning for Predicting Diabetes

Temporal Feature-Based AI Models Excel Over Traditional Mach...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Sasmal, Gopal Charan Gourisaria, Mahendra Kumar Payne, Prasun Patra, Sudhansu Shekhar Kiit Deemed to be University School of Computer Applications Bhubaneswar India Kiit Deemed to be University School of Law Bhubaneswar India Trident Academy of Creative Technology Mca Department Bhubaneswar India Kiit Deemed to be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350360660

Deep learning applied to longitudinal electronic medical record (EMR) data for early diagnosis and prevention of type 2 diabetes mellitus. In order to evaluate the prediction power of deep learning models vs sophisticated machine learning models for temporal risk factors, this study compares both. For predicting diabetes, several deep learning models and characteristics were examined. Over 19,000 patients' longitudinal EMR data from a private hospital were used to apply the framework. Research results show that deep learning models routinely beat conventional machine learning models, obtaining over 91% prediction accuracy without overfitting. Hemoglobin A1c, body mass index, and fasting blood sugar are important indicators of the start of diabetes. With more training data density and patient visit history, the models perform better. In order to improve early diabetes prediction using longitudinal EMR data, this work verifies the effectiveness of LSTM deep learning in incorporating temporal risk aspects. © 2024 IEEE.

关键词： Contrastive Learning

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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Multi-level Feature Fusion for Automated Essay Scoring

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第1期8卷 76-88页

作者： Wang, Jinshui Chen, Junyan Ou, Xuewen Han, Qingfeng Tang, Zhengyi School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China China National Petroleum Corporation China

Automatic Essay Scoring (AES) is one of the significant and challenging research topics in the Natural Language Processing(NLP) area. However, existing AES models majorly consider features derived from vocabulary while failing to integrate sentence and chapter information from multi-level perspectives. In this study, we proposed a multi-level feature fusion model, which was used to capture the multi-level features from different perspectives to improve the accuracy of scoring. Our model consisted of three components to respectively capture the vocabulary-level, sentence-level, and chapter-level features, which were then fed into a CNN and BiLSTM network for the final essay scor-ing. The results show that the proposed model outperforms a set of state-of-the-art AES models on the dataset of the Kaggle Automated Student Assessment Prize (ASAP) com-petition. The average Quadratic Weighted Kappa (QWK) value reaches 0.816, which verifies the efficacy of the model in the task of automated essay scoring. © 2023, Taiwan Ubiquitous Information CO LTD.

关键词： Automation

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Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance applications

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SN computer Science 2025年第2期6卷 1-12页

作者： Mohanapriya, D. Rajalakshmi, K. Helen Josephine, V.L. Panneerselvam, Karthick Department of Computer Science (SF) Sourasthra College Tamil Nadu Madurai India Department of Computer Applications CMR Institute of Technology Karnataka Bangalore India Business Analytics School of Business and Management Christ University Karnataka Bangalore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamilnadu Uthupalayam 641407 India

The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision Loss operator Object tracking Residual network

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Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets

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SN computer Science 2025年第3期6卷 1-30页

作者： Kumar, Vinod Kumar, Ravi Kant Singh, Sunil Kumar School of Computer Applications and Information Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering SRM University AP Andhra Pradesh Guntur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India karnataka Bengaluru India

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalanced Classifiers Machine learning Medical datasets Smote variants

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