DEVELOPMENT OF SKIN CANCER PIGMENT IMAGE CLASSIFICATION USING A COMBINATION OF MOBILENETV2 AND CBAM
DOI:
https://doi.org/10.33480/jitk.v10i4.6541Keywords:
CBAM, CNN, image classification, mobilenet, skin cancerAbstract
Skin cancer is one of the most common types of cancer worldwide, making early detection a crucial factor in improving patient recovery rates. This study compares three classification methods for pigmented skin cancer images using a combination of VGG16 with CBAM, MobileNetV2 with CBAM, and a hybrid VGG16-MobileNetV2 approach with transfer learning. The dataset used in this study is the Skin Cancer ISIC - The International Skin Imaging Collaboration (HAM10000) from Kaggle, which consists of 10,015 images covering seven types of skin cancer. After balancing, the dataset was reduced to 2,400 images with three main classes: Actinic Keratosis (AKIEC), Basal Cell Carcinoma (BCC), and melanoma (MEL), each containing 800 images. This study involves data preprocessing stages such as augmentation, normalization, and image resizing to ensure optimal data quality. The model training process was conducted using the Adam optimizer, a batch size of 16, and an Early Stopping mechanism to prevent overfitting. Evaluation results indicate that the MobileNetV2 with CBAM model achieved the best performance with a validation accuracy of 86%, followed by the VGG16-MobileNetV2 combination at 77%, while VGG16 with CBAM experienced overfitting with an accuracy of 54%. Additionally, the best-performing model demonstrated a precision of 86.53% and a recall of 86.46%, highlighting its superior stability in detecting skin cancer compared to previous single-model approaches. With these results, the developed system can serve as an effective tool for medical professionals in performing early and more accurate skin cancer diagnoses
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K. Urban, S. Mehrmal, P. Uppal, R. L. Giesey, and G. R. Delost, “The global burden of skin cancer: A longitudinal analysis from the Global Burden of Disease Study, 1990–2017,” JAAD Int., vol. 2, pp. 98–108, 2021, doi: 10.1016/j.jdin.2020.10.013.
W. Gouda, N. U. Sama, G. Al-Waakid, M. Humayun, and N. Z. Jhanjhi, “Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning,” healthcare, 2022, doi: https://doi.org/10.3390/ healthcare10071183.
R. R. Saputro, J. Apri, and W. A. Saputra, “Journal of Dinda Klasifikasi Penyakit Kanker Kulit Menggunakan Metode Convolutional Neural Network (Studi Kasus: MELanoma),” J. DINDA, vol. 2, no. 1, pp. 52–57, 2022, doi: https://doi.org/10.20895/dinda.v2i1.349.
D. Raval and J. N. Undavia, “A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images,” Healthc. Anal., vol. 3, no. March, p. 100199, 2023, doi: 10.1016/j.health.2023.100199.
M. A. Arshed, S. Mumtaz, M. Ibrahim, S. Ahmed, M. Tahir, and M. Shafi, “Multi-Class Skin Cancer Classification Using Vision Transformer Networks and Convolutional Neural Network-Based Pre-Trained Models,” Inf., vol. 14, no. 7, 2023, doi: 10.3390/info14070415.
H. M. Balaha and A. E. S. Hassan, “Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm,” Neural Comput. Appl., vol. 35, no. 1, pp. 815–853, 2023, doi: 10.1007/s00521-022-07762-9.
D. Yuliana, Fairuz Dyah Widowati, and Muammar Fawwaz, “Analisis Farmakoekonomi Terhadap Regimen Kemoterapi Pasien Kanker Payudara Di Rumah Sakit Universitas Hasanuddin Makassar,” Media Farm., vol. 20, no. 1, pp. 71–83, 2024, doi: 10.32382/mf.v20i1.491.
Z. Li, K. C. Koban, T. L. Schenck, R. E. Giunta, Q. Li, and Y. Sun, “Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends,” J. Clin. Med., vol. 11, no. 22, 2022, doi: 10.3390/jcm11226826.
L. Rey-Barroso, S. Peña-Gutiérrez, C. Yáñez, F. J. Burgos-Fernández, M. Vilaseca, and S. Royo, “Optical technologies for the improvement of skin cancer diagnosis: A review,” Sensors (Switzerland), vol. 21, no. 1, pp. 1–31, 2021, doi: 10.3390/s21010252.
M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, and M. K. Islam, “An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models,” Mach. Learn. with Appl., vol. 5, no. April, p. 100036, 2021, doi: 10.1016/j.mlwa.2021.100036.
D. P. Mawardi, M. Novita, and N. D. Saputro, “Deteksi Awal Klasifikasi Jenis Penyakit Kanker Kulit Dengan Algoritma Convolutional Neural Network (Cnn) Berbasis Mobile Apps,” Adopsi Teknol. dan Sist. Inf., vol. 3, no. 2, pp. 1–6, 2024, doi: https://doi.org/10.30872/atasi.v3i2.2305.
K. Vayadande, A. A. Bhosle, R. G. Pawar, D. J. Joshi, P. A. Bailke, and O. Lohade, “Innovative approaches for skin disease identification in machine learning: A comprehensive study,” Oral Oncol. Reports, vol. 10, no. April, p. 100365, 2024, doi: 10.1016/j.oor.2024.100365.
M. S. Majib, M. D. M. Rahman, T. M. S. Sazzad, N. I. Khan, and S. K. Dey, “VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images,” IEEE Access, vol. 9, pp. 116942–116952, 2021, doi: 10.1109/ACCESS.2021.3105874.
Z. Yang, “Classification of picture art style based on VGGNET,” J. Phys. Conf. Ser., vol. 1774, no. 1, 2021, doi: 10.1088/1742-6596/1774/1/012043.
D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Electron., vol. 10, no. 20, pp. 1–28, 2021, doi: 10.3390/electronics10202470.
S. R. Shah et al., “Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease,” Agronomy, pp. 1–13, 2023, doi: https://doi.org/ 10.3390/agronomy13061633.
S. Iqbal, A. N. Qureshi, J. Li, and T. Mahmood, On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks, vol. 30, no. 5. Springer Netherlands, 2023. doi: 10.1007/s11831-023-09899-9.
L. Gaur, U. Bhatia, N. Z. Jhanjhi, G. Muhammad, and M. Masud, “Medical image-based detection of COVID-19 using Deep Convolution Neural Networks,” Multimed. Syst., vol. 29, no. 3, pp. 1729–1738, 2023, doi: 10.1007/s00530-021-00794-6.
F. A. Shidik, K. Musthofa, A. P. Kartiningtyas, and T. Agustin, “Analisis citra medis untuk identifikasi penyakit mata dengan teknologi convolutional neural networks,” Semin. Basional Amikom Surakarta, no. November, pp. 68–80, 2024.
H. Lokhande and S. R. Ganorkar, “Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices,” Bull. Electr. Eng. Informatics, vol. 14, no. 1, pp. 357–365, 2025, doi: 10.11591/eei.v14i1.8131.
J. Lee, J. Park, and Y. Lee, “Towards Efficient Cancer Detection on Mobile Devices,” IEEE Access, vol. PP, p. 1, 2025, doi: 10.1109/ACCESS.2025.3543838.
G. A. Pamungkas and A. G. Persada, “Implementasi Sistem Reverse Vending Machine (RVM) Berbasis IoT Menggunakan MobileNet SSD untuk Deteksi Objek dan Mekanisme Insentif Poin pada Platform Sampahmas,” J. Indones. Manaj. Inform. dan Komun., vol. 6, no. 1, pp. 414–426, 2025, doi: https://doi.org/10.35870/jimik.v6i1.1224.
M. Zakariah and A. Alnuaim, “Recognizing human activities with the use of Convolutional Block Attention Module,” Egypt. Informatics J., vol. 27, no. August, p. 100536, 2024, doi: 10.1016/j.eij.2024.100536.
N. Sengodan, “CBAM-EfficientNetV2 for Histopathology Image Classification using Transfer Learning and Dual Attention Mechanisms,” arxiv, no. January, pp. 0–6, 2024, doi: https://doi.org/10.48550/arXiv.2410.22392 Focus to learn more.
M. Yin, Z. Chen, and C. Zhang, “A CNN-Transformer Network Combining CBAM for Change Detection in High-Resolution Remote Sensing Images,” Remote Sens., vol. 15, no. 9, pp. 1–26, 2023, doi: 10.3390/rs15092406.
H. Xin and K. Zhang, “Surface Defect Detection With Channel-Spatial Attention Modules and Bi-Directional Feature Pyramid,” IEEE Access, vol. 11, no. July, pp. 88960–88970, 2023, doi: 10.1109/ACCESS.2023.3303897.
W. Y. Hsu and Y. W. Cheng, “EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 1659–1669, 2023, doi: 10.1109/TNSRE.2023.3255233.
B. Chen and Z. Dang, “Fast PCB Defect Detection Method Based on FasterNet Backbone Network and CBAM Attention Mechanism Integrated With Feature Fusion Module in Improved YOLOv7,” IEEE Access, vol. 11, no. July, pp. 95092–95103, 2023, doi: 10.1109/ACCESS.2023.3311260.
F. Dartiko, R. J. Pradana, R. E. Sari, W. Syahputra, and W. K. Oktoeberza, “Klasifikasi Kanker Kulit Berbasis CNN dengan Metode Hybrid Preprocessing,” Med. Tek. J. Tek. Elektromedik Indones., vol. 5, no. 2, pp. 124–132, 2024, doi: 10.18196/mt.v5i2.22675.
Luqman Hakim, Z. Sari, and H. Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 379–385, 2021, doi: 10.29207/resti.v5i2.3001.
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