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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Kajian Manajemen Bisnis Jurnal Adabiyah Pharmauho: Jurnal Farmasi, Sains, dan Kesehatan Jurnal IUS (Kajian Hukum dan Keadilan) PALITA: Journal of Social - Religion Research Equilibrium: Jurnal Ekonomi-Manajemen-Akuntansi Journal of Institution And Sharia Finance Madinah: Jurnal Studi Islam Jurnal Energi Elektrik Journal of the Community Development in Asia KOLONI Aceh International Journal of Science and Technology TAJDID: Jurnal Ilmu Ushuluddin Al-Mustafid: Journal of Quran and Hadith Studies Journal of Computing Theories and Applications Jurnal Ilmiah Akuntansi dan Keuangan
Muhammad Imran
Environmental Engineering and management, Asian Institute of Technology, 58 Moo 9,Paholyothin Highway Klong Luang, Pathumthani, Bangkok, 12120, Thailand
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Journal : Journal of Computing Theories and Applications

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Enhanced Diagnosis of Skin Cancer from Dermoscopic Images Using Alignment Optimized Convolutional Neural Networks and Grey Wolf Optimization Mazhar, Faheem; Aslam, Naeem; Naeem, Ahmad; Ahmad, Haroon; Fuzail, Muhammad; Imran, Muhammad
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11954

Abstract

Skin cancer (SC) is a highly serious kind of cancer that, if not addressed swiftly, might result in the patient’s demise. Early detection of this condition allows for more effective therapy and prevents disease development. Deep Learning (DL) approaches may be used as an effective and efficient tool for SC detection (SCD). Several DL-based algorithms for automated SCD have been reported. However, more efficient models are needed to improve accuracy. As a result, this paper introduces a new strategy for SCD based on Grey Wolf optimization (GWO) methodologies and CNN. The proposed methodology has four stages: preprocessing, segmentation, feature extraction, and classification. The proposed method utilizes a Convolutional Neural Network (CNN) to extract features from Regions of Interest (ROIs). CNN is employed for feature categorization, whereas the GWO approach enhances accuracy by refining edge detection and segmentation. This technique utilizes a probabilistic model to accelerate the convergence of the GWO algorithm. Employing the GWO model to optimize the structure and weight vectors of CNNs can enhance diagnostic accuracy by a minimum of 5%, based on evaluation outcomes. The application of the proposed strategy and its performance comparison with other methods indicate that the proposed method with GWO predicted SC with an average accuracy of 95.11% and without GWO an Accuracy of 92.66%, respectively, enhancing accuracy by a minimum of 2.5% when we train our model with GWO.
Co-Authors Ahmad, Haroon Al-Amin, Al-Amin Andik Bintoro Aniswita Apipah Haeriah, Nur Aslam, Naeem Baba, Muammar Dafit Feriyanto Ezwarsyah Ezwarsyah Fathia Bailusy, Ummi Suci Fuzail, Muhammad Haiqal, Muhammad Hanafi, Muhammad Fujii Hardivizon Hardivizon Hasan, Muttaqin Husain, Muhammad Zakir I Putu Sudayasa, I Putu Indria Hafizah, Indria Izwan, Saiful Jimmy, Yohanes Kee, Daisy Mui Hung Kumar, Sumit Mahmud, Basri Mazhar, Faheem Mochammad Afifuddin MUH. FICHRIYADI HASTIRA Muhamad Fitri Muhammad Fauzi Mujiburrahman Muniandy, Alvesterlee M Naeem, Ahmad Panjaitan*, Arief Prayoga, Dimas Purwandy Hasibuan, Purwandy Rahman, Muhammad Fadli Romahadi, Dedik Rudiansyah Putra Sabandar, Michelle Clysia Shimizu, Masaru Singh, Shagun St Nur Syahidah Dzatun Nurain Sudirman, Muh Sajjaj Surya Bermansyah, Surya Taufani, Taufani Uddu, Waode Sitti Asfiah Wahid Mongkito, Abdul Widya Astuti Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Yakin, Aynul