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All Journal Rekursif: Jurnal Informatika Communications in Science and Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Medika Teknika : Jurnal Teknik Elektromedik Indonesia
Oktoeberza, Widhia KZ
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Comparative Study of Deep Learning Models to Classify of Multi-Class Skin Cancer on Imbalanced Data Oktoeberza, Widhia KZ; Rahman, Muhammad Farchan Al; Vasiguhamiaz, Azvadennys; Huda, Widya Nurul; Mainil, Afdhal Kurniawan; Sari, Julia Purnama
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85544

Abstract

Skin cancer diagnosis faces challenges in efficiency and accuracy. This research addresses the need for improved non-invasive diagnostic tools by leveraging deep learning for multi-class skin cancer classification from dermoscopic images. A key focus is overcoming the limitations of imbalanced datasets, common in medical imaging, which can hinder model performance. We propose an optimal strategy utilizing a Convolutional Neural Network (CNN) transfer learning methodology. The process involves CNN-based segmentation to isolate relevant regions, followed by feature extraction and classification. We comparatively evaluated three pre-trained transfer learning techniques: DenseNet201, ResNet50, and VGG16, using the HAM10000 dataset (10,015 images across seven skin cancer classes). To mitigate severe class imbalance, Random Oversampling was employed, chosen for its simplicity and effectiveness in balancing the dataset and enhancing model generalization. Model performance was rigorously evaluated using accuracy, precision, recall, and F1-score. DenseNet201 consistently achieved superior performance, with an accuracy of 97% post-oversampling. It also exhibited the highest precision, recall, and F1-score across all models, confirming its effectiveness in classifying both majority and minority classes. Compared to previous studies on HAM10000, our DenseNet201 model's test accuracy of 96.52% is competitive or superior to reported accuracy of 90-92%. This highlights the synergistic effect of DenseNet201's efficient feature reuse and robust data balancing. This research provides a robust framework for advanced methodologies in skin cancer classification, particularly for imbalanced medical image datasets.
Co-Authors Aan Erlansari Afdhal Kurniawan Mainil Aliana, Mareta Butar Butar, Federika Dartiko, Fedryanto Desi Andreswari Dwi Jayanto, Paksi Ernawati Ernawati Funny Farady Coastera Gita Fitria, Nabila Hanung Adi Nugroho Hijrayanti, Hikmah Hotama, Christianus Frederick Huda, Widya Nurul Igi Ardiyanto Ikhsan, Arif Rahmat Indah Soesanti Julia Purnama Sari Julia Purnama Sari Listyalina, Latifah Luthfi, Irfan Muthi, Marsa Hulwa Indri Pradana, Refki Jorgi Rahman, Muhammad Farchan Al Saputri, Dian Ardiyanti Sari, Renti Epana Sunu Wibirama Syahputra, Wahyu Vasiguhamiaz, Azvadennys Yulyanti, Vesi Zarah Juaita, Aisyah Amelia