Dermatological Decision Support Systems using CNN for Binary Classification
Received: 5 March 2024 | Revised: 26 March 2024 | Accepted: 28 March 2024 | Online: 13 April 2024
Corresponding author: Rajendra Dev Dondapati
Abstract
Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.
Keywords:
ISIC 2017, VGG16, ResNet50, InceptionV3Downloads
References
U. B. Ansari and T. Sarode, "Skin Cancer Detection Using Image Processing," International Research Journal of Engineering and Technology, vol. 4, no. 4, pp. 2875–2882, Apr. 2017.
B. Cassidy, C. Kendrick, A. Brodzicki, J. Jaworek-Korjakowska, and M. H. Yap, "Analysis of the ISIC image datasets: Usage, benchmarks and recommendations," Medical Image Analysis, vol. 75, Jan. 2022, Art. no. 102305.
A. Yilmaz, M. Kalebasi, Y. Samoylenko, M. E. Guvenilir, and H. Uvet, "Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset." arXiv, Oct. 23, 2021.
T. Imran, A. S. Alghamdi, and M. S. Alkatheiri, "Enhanced Skin Cancer Classification using Deep Learning and Nature-based Feature Optimization," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12702–12710, Feb. 2024.
V. A. Rajendran and S. Shanmugam, "Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12734–12739, Feb. 2024.
K. Rajeshkumar, C. Ananth, and N. Mohananthini, "Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10978–10983, Jun. 2023.
Y. Jusman, I. M. Firdiantika, D. A. Dharmawan, and K. Purwanto, "Performance of Multi Layer Perceptron and Deep Neural Networks in Skin Cancer Classification," in 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), Nara, Japan, Mar. 2021, pp. 534–538.
H. Tabrizchi, S. Parvizpour, and J. Razmara, "An Improved VGG Model for Skin Cancer Detection," Neural Processing Letters, vol. 55, no. 4, pp. 3715–3732, Aug. 2023.
A. Panthakkan, S. M. Anzar, S. Jamal, and W. Mansoor, "Concatenated Xception-ResNet50 — A novel hybrid approach for accurate skin cancer prediction," Computers in Biology and Medicine, vol. 150, Nov. 2022, Art. no. 106170.
S. ElGhany, M. Ibraheem, M. Alruwaili, and M. Elmogy, "Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network," Computers, Materials & Continua, vol. 68, no. 1, pp. 117–135, 2021.
A. Mehra, A. Bhati, A. Kumar, and R. Malhotra, "Skin Cancer Classification Through Transfer Learning Using ResNet-50," in Emerging Technologies in Data Mining and Information Security, Singapore, 2021, pp. 55–62.
T. Cao, "Skin cancer image classification optimization through transfer learning with Tensorflow and InceptionV3," in International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), Mar. 2023, vol. 12596, pp. 449–458.
C. P Manju, J. P. Jeslin, and V. Vinitha, "Computer-Aided Detection of Skin Cancer Detection from Lesion Images Via Deep Learning Techniques: 3d CNN Integrated Inception V3 Networks," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 550–562, Jul. 2023.
D. Gutman et al., "Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)." arXiv, May 04, 2016.
A. Naeem, M. S. Farooq, A. Khelifi, and A. Abid, "Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities," IEEE Access, vol. 8, pp. 110575–110597, 2020.
U. Leiter, U. Keim, and C. Garbe, "Epidemiology of Skin Cancer: Update 2019," in Sunlight, Vitamin D and Skin Cancer, J. Reichrath, Ed. Cham, Switzerland: Springer International Publishing, 2020, pp. 123–139.
A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, and M. Lang, "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, vol. 143, Mar. 2020, Art. no. 106839.
J. Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering. Boca Raton, FL, USA: CRC Press, 2020.
A. Selvia, V. N. Prakash, N. Saravanan, B. Jawahar, and V. Karthick, "Skin Lesion Detection Using Feature Extraction Approach," Annals of the Romanian Society for Cell Biology, pp. 3939–3951, Apr. 2021.
V. Anand, S. Gupta, A. Altameem, S. R. Nayak, R. C. Poonia, and A. K. J. Saudagar, "An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer," Diagnostics, vol. 12, no. 7, Jul. 2022, Art. no. 1628.
H. Qassim, A. Verma, and D. Feinzimer, "Compressed residual-VGG16 CNN model for big data places image recognition," in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Jan. 2018, pp. 169–175.
T. Emara, H. M. Afify, F. H. Ismail, and A. E. Hassanien, "A Modified Inception-v4 for Imbalanced Skin Cancer Classification Dataset," in 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, Dec. 2019, pp. 28–33.
S. Likhitha and R. Baskar, "Skin Cancer Segmentation Using R-CNN Comparing with Inception V3 for Better Accuracy," in 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, Dec. 2022, pp. 1293–1297.
Md. A. Mamun, Md. S. Kabir, M. Akter, and M. S. Uddin, "Recognition of human skin diseases using inception-V3 with transfer learning," International Journal of Information Technology, vol. 14, no. 6, pp. 3145–3154, Oct. 2022.
O. R. Devi, S. Aarathi, and O. Sirisha, "An Efficient Approach for classification of skin cancer lesions using Inception V3," NeuroQuantology, vol. 20, no. 10, pp. 7361–7371, Aug. 2022.
M. Attique Khan, M. Sharif, T. Akram, S. Kadry, and C.-H. Hsu, "A two-stream deep neural network-based intelligent system for complex skin cancer types classification," International Journal of Intelligent Systems, vol. 37, no. 12, pp. 10621–10649, 2022.
A. Lembhe, P. Motarwar, R. Patil, and S. Elias, "Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network," Procedia Computer Science, vol. 218, pp. 164–173, Jan. 2023.
E. D. Pulakos, "A comparison of rater training programs: Error training and accuracy training," Journal of Applied Psychology, vol. 69, no. 4, pp. 581–588, 1984.
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