A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection</title> <meta name="description" content="A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection"> <meta name="keywords" content="Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection"> <meta name="citation_author" content="Hritwik Ghosh"> <meta name="citation_author" content="Irfan Sadiq Rahat"> <meta name="citation_author" content="Sachi Nandan Mohanty"> <meta name="citation_author" content="J. V. R. Ravindra"> <meta name="citation_author" content="Abdus Sobur"> <meta name="citation_publication_date" content="2024/01/11"> <meta name="citation_journal_title" content="International Journal of Computer and Systems Engineering"> <meta name="citation_volume" content="18"> <meta name="citation_issue" content="1"> <meta name="citation_firstpage" content="51"> <meta name="citation_lastpage" content="59"> <meta name="citation_pdf_url" content="https://publications.waset.org/10013461/pdf"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value=""> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 33093</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hritwik%20Ghosh">Hritwik Ghosh</a>, <a href="https://publications.waset.org/search?q=Irfan%20Sadiq%20Rahat"> Irfan Sadiq Rahat</a>, <a href="https://publications.waset.org/search?q=Sachi%20Nandan%20Mohanty"> Sachi Nandan Mohanty</a>, <a href="https://publications.waset.org/search?q=J.%20V.%20R.%20Ravindra"> J. V. R. Ravindra</a>, <a href="https://publications.waset.org/search?q=Abdus%20Sobur"> Abdus Sobur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.</p> <iframe src="https://publications.waset.org/10013461.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=skin%20cancer" title=" skin cancer"> skin cancer</a>, <a href="https://publications.waset.org/search?q=dermatology" title=" dermatology"> dermatology</a>, <a href="https://publications.waset.org/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/search?q=healthcare%20technology" title=" healthcare technology"> healthcare technology</a>, <a href="https://publications.waset.org/search?q=cancer%20detection" title=" cancer detection"> cancer detection</a>, <a href="https://publications.waset.org/search?q=medical%20imaging." title=" medical imaging."> medical imaging.</a> </p> <a href="https://publications.waset.org/10013461/a-study-on-the-application-of-machine-learning-and-deep-learning-techniques-for-skin-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013461/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013461/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013461/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013461/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013461/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013461/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013461/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013461/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013461/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013461/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013461.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1442</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Thomas, S. (2022). Deep learning methods for the characterisation of non-melanoma skin cancer (The University of Queensland, Institute for Molecular Bioscience). https://doi.org/10.14264/892097 <br>[2] Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R. A., Rehman, A., Iqbal, M., & Saba, T. (2022). Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering. Microscopy Research and Technique, 85(1), 339–351. https://doi.org/10.1002/jemt.23908 <br>[3] Khamparia, A., Singh, P. K., Rani, P., Samanta, D., Khanna, A., & Bhushan, B. (2021). An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning. Transactions on Emerging Telecommunications Technologies, 32(7), n/a–n/a. https://doi.org/10.1002/ett.3963 <br>[4] Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021). Skin Cancer Detection: A Review Using Deep Learning Techniques. International Journal of Environmental Research and Public Health, 18(10), 5479. https://doi.org/10.3390/ijerph18105479 <br>[5] S, R., J., P., Quadir Md, A., Jackson J, C., Sharma, S., & B., R. (2022). Skin Cancer Detection using Deep Learning. Research Journal of Pharmacy and Technology, 15(10), 4519–4525. https://doi.org/10.52711/0974-360X.2022.00758 <br>[6] Garcia, S. I. (2021). Meta-learning for skin cancer detection using Deep Learning Techniques. https://doi.org/10.48550/arxiv.2104.10775 <br>[7] Gomathi, E., Jayasheela, M., Thamarai, M., & Geetha, M. (2023). Skin cancer detection using dual optimization based deep learning network. Biomedical Signal Processing and Control, 84, 104968. https://doi.org/10.1016/j.bspc.2023.104968 <br>[8] Sivakumar, N. R., Sara Abdelwahab Ghorashi, Faten Khalid Karim, Alabdulkreem, E., & Al-Rasheed, A. (2022). MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning. Computers, Materials & Continua, 73(3), 6253. https://doi.org/10.32604/cmc.2022.029266 <br>[9] Balambigai, S., Elavarasi, K., Abarna, M., Abinaya, R., & Arun Vignesh, N. (2022). Detection and optimization of skin cancer using deep learning. Journal of Physics. Conference Series, 2318(1), 12040. https://doi.org/10.1088/1742-6596/2318/1/012040 <br>[10] Fraiwan, M., & Faouri, E. (2022). On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning. Sensors (Basel, Switzerland), 22(13), 4963. https://doi.org/10.3390/s22134963 <br>[11] Pan, J.-S., Meng, Z., Li, J., & Virvou, M. (2022). Early Detection of Melanoma Skin Cancer Using Image Processing and Deep Learning. In Advances in Intelligent Information Hiding and Multimedia Signal Processing (Vol. 278). Springer. https://doi.org/10.1007/978-981-19-1053-1_25 <br>[12] Tembhurne, J. V., Hebbar, N., Patil, H. Y., & Diwan, T. (2023). Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-14697-3 <br>[13] Wang, X., Yang, Y., & Mandal, B. (2023). Automatic detection of skin cancer melanoma using transfer learning in deep network. AIP Conference Proceedings, 2562(1). https://doi.org/10.1063/5.0111909 <br>[14] Daghrir, J., Tlig, L., Bouchouicha, M., & Sayadi, M. (2020). Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach. 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 1–5. https://doi.org/10.1109/ATSIP49331.2020.9231544 <br>[15] Singh, H., Kaushik, S., Talyan, S., & Dwivedi, K. (2022). Skin Cancer Detection Using Deep Learning techniques. International Journal for Research in Applied Science and Engineering Technology, 10(5), 4296–4305. https://doi.org/10.22214/ijraset.2022.43090 <br>[16] Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 <br>[17] Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. 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A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection