Implementation of Deep Transfer Learning and Explainable AI in Skin Cancer Classification

Muhammad Eky Ramadhan, Junta Zeniarja

Abstract


Kanker kulit merupakan salah satu penyakit yang prevalensinya terus meningkat, khususnya di wilayah dengan paparan sinar ultraviolet (UV) tinggi. Tantangan utama diagnosis kanker kulit terletak pada kemiripan visual antara lesi jinak dan ganas, menyebabkan tingginya tingkat kesalahan diagnosis, bahkan oleh tenaga medis berpengalaman. Pendekatan diagnosis berbasis kecerdasan buatan (AI) saat ini masih dianggap sebagai sistem “black box” karena kurangnya transparansi dalam proses pengambilan keputusan. Studi ini bertujuan untuk mengembangkan sistem klasifikasi kanker kulit berbasis deep transfer learning yang terintegrasi dengan Explainable Artificial Intelligence (XAI). Model pretrained CNN, ResNet101 dan DenseNet201 diterapkan untuk klasifikasi citra dari dataset ISIC2020 dalam dua kelas, yaitu benign dan malignant. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, F1-score, confusion matrix, dan kurva ROC. Hasil penelitian menunjukkan bahwa ResNet101 memberikan performa klasifikasi terbaik dengan accuracy 87,00%, AUC 0,943, dan recall 89,6% dalam mendeteksi malignant. Untuk mendukung interpretabilitas, teknik Grad-CAM dan LIME diterapkan untuk visualisasi area penting dalam citra yang menjadi dasar keputusan model. Temuan ini menekankan pendekatan yang menjanjikan untuk mendukung sistem pendukung keputusan medis yang lebih akurat, transparan, dan dapat dijelaskan, khususnya dalam konteks diagnosis kanker kulit.

Keywords


image classification;skin cancer;transfer learning;Grad-CAM;explainable AI

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References


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DOI: https://doi.org/10.32520/stmsi.v14i5.5425

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