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Hersatoto Listiyono
Universitas Stikubank
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Enhancing skin cancer detection using transfer learning and AdaBoost: a deep learning approach Listiyono, Hersatoto; Retnowati, Retnowati; Purwatiningtyas, Purwatiningtyas; Nur Wahyudi, Eko; Maskur, Ali
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10379

Abstract

Skin cancer is one of the most prevalent types of cancer worldwide, with early detection playing a critical role in improving patient outcomes. In this study, we propose a deep learning model based on LeNet-7 combined with adaptive boosting (AdaBoost) to classify skin lesions as either benign or malignant using the International Skin Imaging Collaboration (ISIC) dataset. We evaluate the proposed model alongside other well-established deep learning architectures, such as residual network (ResNet), VGGNet, and the traditional LeNet model, through various performance metrics including precision, recall, F1-score, specificity, Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and testing accuracy. Our results demonstrate that the proposed model (LeNet-7+AdaBoost) significantly outperforms the other models, achieving a testing accuracy of 91.3%, precision of 0.92, recall of 0.91, and AUC-ROC of 0.93. The model successfully addresses issues of overfitting and generalization, providing a robust solution for skin cancer classification. However, some misclassifications of visually similar benign and malignant lesions highlight areas for future improvement. The proposed model shows promise in real-world medical applications and paves the way for further research into optimizing deep learning models for skin cancer detection.
Co-Authors Abdul Karim Adelia Aura Diva Zainanda Agus Budi Santosa Agus Perdana Windarto Agus Prasetyo Agus Prasetyo Utomo Agus Prasetyo Utomo Aji Supriyanto Alana Sharfina Wedaningsih Ali Maskur Alif Budi Santoso Anak Agung Istri Sri Wiadnyani Anisa Istiqomah Antono Adhi Askar Yunianto Budiarso, Zuly Budiarso Daniswara, Alfreda Khansa Diah Laila Sani Dwi Agus Diartono Eddy Nurraharjo Eddy Nurraharjo Edi Supriyanto, Edi Edy Supriyanto Edy Winarno Eko Nur Wahyudi Eri Zuliarso Ferdiansyah, Alif Hari Murti Heribertus Yulianton Herny Februariyanti Herny Februariyanti Ibrahim, A. F. Indriani, Vanyariska Isworo Nugroho Isworo Nugroho Jati Sasongko Wibowo Jeffri Alfa Razaq Liana Rahmaziana Muhammad A.R. Hidayat Muji Sukur Novita Mariana P Purwatiningtyas Priambodo, Ilyas Purwadi, Dimas Indra Purwaningtyas , P Purwatiningtyas Purwatiningtyas Purwatiningtyas Purwatiningtyas Purwatiningtyas, P R. Soelistijadi Raharjo, Eddy Nur Ramadhan, Guntur Rani, R.P.S. Retnowati Retnowati Retnowati Retnowati Retnowati Rizal Adi Saputro Rosyida, Elviana S Sunardi S Sunardi Saefurrahman, Saefurrahman Sariyun Naja Anwar Sekar Ayu Ningtyas Sri Mulyani Sri Mulyani Sri Mulyani Sri Yulianto Fajar Pradapa Sugiyamto Sugiyamto Sugiyamto Sugiyamto Sugiyamto Sugiyamto, Sugiyamto Sunardi sunardi sunardi Sunardi Sunardi Sunardi Sunardi Sunardi Sunardi Susi Susilowati, Susi Syamsul Arifin Teguh Khristianto Teguh Kristianto Vici Tiara Anjarsari Widiyanto Tri Handoko Yunus Anis Yunus Anis, Yunus Ziana, Liana Rahma Zuly Budiarso Zuly Budiharso