Deep Transfer Learning untuk Meningkatkan Akurasi Klasifikasi pada Citra Dermoskopi Kanker Kulit
Abstrak
Kanker kulit benign (jinak) dan malignant (ganas) merupakan jenis kanker kulit yang sering dijumpai. Tanda-tanda kanker kulit penting untuk diketahui dengan diagnosis dini agar dapat diberikan penanganan yang tepat, sehingga dapat mengurangi tingkat kematian penderitanya. Citra dermoskopi menjadi salah satu media diagnosis yang telah banyak dikembangkan oleh peneliti. Analisis citra dermoskopi memberikan hasil lebih optimal dalam diagnosis berbasis komputasi dibandingkan deteksi visual. Model yang berhasil diterapkan dalam diagnosis berbasis komputasi tersebut di antaranya deep learning dan transfer learning, tetapi masih diperlukan optimalisasi. Pada penelitian ini, dilakukan klasifikasi citra dermoskopi kanker kulit ke dalam dua kelas (benign dan malignant) dengan memanfaatkan transfer learning. Untuk meningkatkan akurasi penelitian yang telah dilakukan sebelumnya pada dataset publik dari Kaggle dengan jumlah 3.297 citra, penelitian ini menggunakan 2.000 citra. Penelitian ini membandingkan dua pre-trained model, yaitu VGG-16 dan residual network (ResNet)-50 yang digunakan sebagai feature extractor. Selanjutnya dilakukan fine-tuning dengan menambahkan flatten layer, dua dense layer dengan fungsi aktivasi ReLU, dan satu dense layer dengan fungsi aktivasi Softmax untuk melakukan klasifikasi ke dalam dua kelas. Hyper parameter tuning pada optimizer, batch size, learning rate, dan epoch dilakukan untuk mendapatkan kombinasi parameter dengan kinerja terbaik pada masing-masing model. Sebelum dilakukan hyper parameter tuning, model diuji dengan resize citra masukan menggunakan ukuran yang berbeda-beda. Hasil pengujian model menggunakan citra uji pada model VGG-16 memberikan kinerja terbaiknya pada ukuran citra 128 × 128 piksel dengan kombinasi parameter Adam sebagai optimizer, batch size 64, learning rate 0,001, dan epoch 10 dengan nilai akurasi 91% dan loss 0,2712. Model ResNet-50 memberikan akurasi yang lebih baik, yaitu 94%, dan loss 0,2198 dengan parameter optimizer RMSprop, batch size 64, learning rate 0,0001, dan epoch 100. Hasil pengujian ini menunjukkan bahwa metode yang diusulkan memberikan akurasi yang baik dan dapat membantu dermatologi dalam deteksi dini kanker kulit.
Referensi
WHO (2022) “Cancer,” [Online], https://www.who.int/news-room/fact-sheets/detail/cancer, access date: 7-Jun-2022.
M.S. Ali et al., “An Enhanced Technique of Skin Cancer Classification Using Deep Convolutional Neural Network with Transfer Learning Models,” Mach. Learn. Appl., Vol. 5, pp. 1–8, Sep. 2021, doi: 10.1016/j.mlwa.2021.100036.
M.A. Kassem, K.M. Hosny, dan M.M. Fouad, “Skin Lesions Classification into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning,” IEEE Access, Vol. 8, pp. 114822–114832, Jun. 2020, doi: 10.1109/ACCESS.2020.3003890.
M.R. Hasan et al., “Comparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks,” J. Healthc. Eng., Vol. 2021, pp. 1–17, 2021, doi: 10.1155/2021/5895156.
P.P. Naik, “Cutaneous Malignant Melanoma: A Review of Early Diagnosis and Management,” World J. Oncol., Vol. 12, No. 1, pp. 7–19, Feb. 2021, doi: 10.14740/wjon1349.
S. Bechelli dan J. Delhommelle, “Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images,” Bioeng., Vol. 9, No. 3, pp. 1–18, Mar. 2022, doi: 10.3390/bioengineering9030097.
T.J. Brinker et al., “Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review,” J. Med. Internet Res., Vol. 20, No. 10, pp. 1–8, Oct. 2018, doi: 10.2196/11936.
P.M.M. Pereira et al., “Skin Lesion Classification Enhancement Using Border-Line Features – The Melanoma vs Nevus Problem,” Biomed. Signal Process. Control, Vol. 57, pp. 1-8, Mar. 2020, doi: 10.1016/j.bspc.2019.101765.
G. Argenziano et al., “Dermoscopy Improves Accuracy of Primary Care Physicians to Triage Lesions Suggestive of Skin Cancer,” J. Clin. Oncol., Vol. 24, No. 12, pp. 1877–1882, Apr. 2006, doi: 10.1200/JCO.2005.05.0864.
S.M. Jaisakthi, P. Mirunalini, dan C. Aravindan, “Automated Skin Lesion Segmentation Of Dermoscopic Images Using Grabcut And K‐Means,” IET Comput. Vis., Vol. 12, No. 8, pp. 1088–1095, Dec. 2018, doi: 10.1049/iet-cvi.2018.5289.
R. Agustina, R. Magdalena, dan N.K.C. Pratiwi, “Klasifikasi Kanker Kulit Menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., Vol. 10, No. 2, pp. 446–457, Apr. 2022, doi: 10.26760/elkomika.v10i2.446.
V. Anand et al., “An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer,” Diagnostics, Vol. 12, No. 7, Jul. 2022, doi: 10.3390/diagnostics12071628.
Y. Wu et al., “Skin Cancer Classification With Deep Learning: A Systematic Review,” Front. Oncol., Vol. 12, pp. 1–20, Jul. 2022, doi: 10.3389/fonc.2022.893972.
M.A. Kassem, K.M. Hosny, R. Damaševičius, dan M.M. Eltoukhy, “Machine learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review,” Diagnostics, Vol. 11, No. 8, pp. 1–29, Jul. 2021, doi: 10.3390/diagnostics11081390.
A. Saber et al., “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, Vol. 9, pp. 71194–71209, May 2021, doi: 10.1109/ACCESS.2021.3079204.
R. Refianti, A.B. Mutiara, dan R.P. Priyandini, “Classification of Melanoma Skin Cancer Using Convolutional Neural Network,” Int. J. Adv. Comput. Sci., Appl., Vol. 10, No. 3, pp. 409–417, Mar. 2019, doi: 10.14569/IJACSA.2019.0100353.
S. Saidah, I.P.Y.N. Suparta, and E. Suhartono, “Modifikasi Convolutional Neural Network Arsitektur GoogLeNet dengan Dull Razor Filtering untuk Klasifikasi Kanker Kulit,” J. Nas. Tek. Elekt., Teknol. Inf., Vol. 11, No. 2, pp. 148–153, May 2022, doi: 10.22146/jnteti.v11i2.2739.
H.A. Haenssle et al., “Man Against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists,” Ann. Oncol., Vol. 29, No. 8, pp. 1836–1842, Aug. 2018, doi: 10.1093/annonc/mdy166.
T.J. Brinker et al., “A Convolutional Neural Network Trained with Dermoscopic Images Performed on Par With 145 Dermatologists in a Clinical Melanoma Image Classification Task,” Eur. J. Cancer, Vol. 111, pp. 148–154, Apr. 2019, doi: 10.1016/j.ejca.2019.02.005.
S. Guan and M. Loew, “Breast Cancer Detection Using Transfer Learning In Convolutional Neural Networks,” 2017 Appl. Imag. Pattern Recognit. Workshop (AIPR), 2017, pp. 1–8, doi: 10.1109/AIPR.2017.8457948.
M. Hussain, J.J. Bird, dan D.R. Faria, “A Study on CNN Transfer Learning for Image Classification,” in Advances in Computational Intelligence Systems, A. Lotfi et al., Eds., Cham, Switzerland: Springer Cham, 2019, pp. 191–202, doi: 10.1007/978-3-319-97982-3_16.
S. Tammina, “Transfer Learning Using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” Int. J. Sci., Res. Publ., Vol. 9, No. 10, pp. 143–150, Oct. 2019, doi: 10.29322/ijsrp.9.10.2019.p9420.
Shallu dan R. Mehra, “Breast Cancer Histology Images Classification: Training from Scratch or Transfer Learning?” ICT Express, Vol. 4, No. 4, pp. 247–254, Dec. 2018, doi: 10.1016/j.icte.2018.10.007.
D.M. Wonohadidjojo, “Perbandingan Convolutional Neural Network pada Transfer Learning Method untuk Mengklasifikasikan Sel Darah Putih,” Ultimics J. Tek. Inform., Vol. 13, No. 1, pp. 51–57, Jun. 2021, doi: 10.31937/ti.v13i1.2040.
Z. Aliyah, A. Arifianto, and F. Sthevanie, “Classifying Skin Cancer in Digital Images Using Convolutional Neural Network with Augmentation,” Indonesia J. Comput. (Indo-JC), Vol. 5, No. 2, pp. 55–66, Sep. 2020, doi: 10.21108/indojc.2020.5.2.455.
A. Abubakar, M. Ajuji, and I.U. Yahya, “Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination,” Appl. Syst. Innov., Vol. 3, No. 2, pp. 1–15, Apr. 2020, doi: 10.3390/asi3020020.
A.V. Ikechukwu, S. Murali, R. Deepu, and R. C. Shivamurthy, “ResNet-50 vs VGG-19 vs Training from Scratch: A Comparative Analysis of the Segmentation and Classification of Pneumonia from Chest X-Ray Images,” Glob. Transit. Proc., Vol. 2, No. 2, pp. 375–381, Nov. 2021, doi: 10.1016/j.gltp.2021.08.027.
R.A. Khan, A. Crenn, A. Meyer, and S. Bouakaz, “A Novel Database of Children’s Spontaneous Facial Expressions (LIRIS-CSE),” Image, Vis. Comput., Vol. 83–84, pp. 61–69, Mar.–Apr. 2019, doi: 10.1016/j.imavis.2019.02.004.
C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, “Activation Functions: Comparison of Trends in Practice and Research for Deep Learning,” 2018, arXiv:1811.03378.
X. Qin et al., “A Faster R-CNN Based Method for Comic Characters Face Detection,” 2017 14th IAPR Int. Conf. Doc. Anal., Recogn. (ICDAR), 2017, pp. 1074–1080, doi: 10.1109/ICDAR.2017.178.
M. Korpusik and J. Glass, “Convolutional Neural Networks and Multitask Strategies for Semantic Mapping of Natural Language Input to a Structured Database,” 2018 IEEE Int. Conf. Acoust. Speech, Signal Process. (ICASSP), 2018, pp. 6174–6178, doi: 10.1109/ICASSP.2018.8461769.
E. Solovyeva and A. Abdullah, “Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network,” Inventions, Vol. 6, No. 4, Oct. 2021, doi: 10.3390/inventions6040070.
K. Manasa and D.G.V. Murthy, “Skin Cancer Detection Using VGG-16,” Eur. J. Mol., Clin. Med., Vol. 8, No. 1, pp. 1419–1426, Jan. 2021.
M. Faruk and N. Nafi’iyah, “Klasifikasi Kanker Kulit Berdasarkan Fitur Tekstur, Fitur Warna Citra Menggunakan SVM dan KNN,” Telematika, Vol. 13, No. 2, pp. 100–109, Aug. 2020, doi: 10.35671/telematika.v13i2.987.
N. Khasanah et al., “Skin Cancer Classification Using Random Forest Algorithm,” J. Ilm. Sisfotenika, Vol. 11, No. 2, pp. 137–147, Jul. 2021, doi: 10.30700/jst.v11i2.1122.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.
2.png)
