Machine Learning Mastery: Leveraging Convolutional Neural Networks to Classify Skin Cancers as Benign or Malignant in the ISIC Database


  • Upendra Singh, Krupa Purohit, Chitralekha Dwivedi, Ritu Patidar, Sanjay Patidar


Convolutional Neural Networks , ISIC database , Skin Cancers , Hidden Layers, Benign, Malignant, Machine Learning.


This research tackles the urgent need for enhanced precision in the detection of skin cancer, a common yet potentially deadly disease. Traditional diagnostic techniques frequently fall short in accuracy, prompting unnecessary and invasive medical interventions. Previous attempts to employ machine learning for distinguishing among different types of skin cancer have not been fully successful in achieving effective differentiation. To address these challenges, the study proposes an innovative approach utilizing Convolutional Neural Networks (CNN) for the autonomous identification of skin cancer. The designed CNN architecture incorporates three hidden layers, with the number of channels in each layer progressively increasing from 16 to 32, and then to 64. The model leverages the AdamW optimization algorithm with a learning rate set at 0.001, a choice that has proven to be highly effective. In evaluations conducted using the International Skin Imaging Collaboration (ISIC) dataset, which involved classifying skin lesions as either benign or malignant, the proposed CNN methodology demonstrated a remarkable accuracy rate of 96%. This level of precision indicates a significant advancement in the field of skin cancer diagnostics, highlighting the potential of CNN-based models to revolutionize the early detection and treatment of this condition.


Download data is not yet available.


B. Bılgıç, "Comparison of Breast Cancer and Skin Cancer Diagnoses Using Deep Learning Method," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477992.

A. Mirbeik-Sabzevari and N. Tavassolian, "Ultrawideband, Stable Normal and Cancer Skin Tissue Phantoms for Millimeter-Wave Skin Cancer Imaging," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 1, pp. 176-186, Jan. 2019, doi: 10.1109/TBME.2018.2828311.

C. Aydinalp, S. Joof, T. Yilmaz, N. P. Özsobaci, F. A. Alkan and I. Akduman, "In Vitro Dielectric Properties of Rat Skin Tissue for Microwave Skin Cancer Detection," 2019 International Applied Computational Electromagnetics Society Symposium (ACES), 2019, pp. 1-2.

H. Younis, M. H. Bhatti and M. Azeem, "Classification of Skin Cancer Dermoscopy Images using Transfer Learning," 2019 15th International Conference on Emerging Technologies (ICET), 2019, pp. 1-4, doi: 10.1109/ICET48972.2019.8994508.

Y. Jusman, I. M. Firdiantika, D. A. Dharmawan and K. Purwanto, "Performance of Multi Layer Perceptron and Deep Neural Networks in Skin Cancer Classification," 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), 2021, pp. 534-538, doi: 10.1109/LifeTech52111.2021.9391876.

A. W. Setiawan, "Effect of Color Enhancement on Early Detection of Skin Cancer using Convolutional Neural Network," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020, pp. 100-103, doi: 10.1109/ICIoT48696.2020.9089631.

A. W. Setiawan, A. Faisal and N. Resfita, "Effect of Image Downsizing and Color Reduction on Skin Cancer Pre-screening," 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2020, pp. 148-151, doi: 10.1109/ISITIA49792.2020.9163734.

H. K. Kondaveeti and P. Edupuganti, "Skin Cancer Classification using Transfer Learning," 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), 2020, pp. 1-4, doi: 10.1109/ICATMRI51801.2020.9398388.

N. Shafi et al., "A Portable Non-Invasive Electromagnetic Lesion-Optimized Sensing Device for the Diagnosis of Skin Cancer (SkanMD)," in IEEE Transactions on Biomedical Circuits and Systems, vol. 17, no. 3, pp. 558-573, June 2023.

K. Mridha, M. M. Uddin, J. Shin, S. Khadka and M. F. Mridha, "An Interpretable Skin Cancer Classification Using Optimized Convolutional Neural Network for a Smart Healthcare System," in IEEE Access, vol. 11, pp. 41003-41018, 2023.

R. Schiavoni, G. Maietta, E. Filieri, A. Masciullo and A. Cataldo, "Microwave Reflectometry Sensing System for Low-Cost in-vivo Skin Cancer Diagnostics," in IEEE Access, vol. 11, pp. 13918-13928, 2023.

L. Riaz et al., "A Comprehensive Joint Learning System to Detect Skin Cancer," in IEEE Access, vol. 11, pp. 79434-79444, 2023.

H. L. Gururaj, N. Manju, A. Nagarjun, V. N. M. Aradhya and F. Flammini, "DeepSkin: A Deep Learning Approach for Skin Cancer Classification," in IEEE Access, vol. 11, pp. 50205-50214, 2023.

A. Imran, A. Nasir, M. Bilal, G. Sun, A. Alzahrani and A. Almuhaimeed, "Skin Cancer Detection Using Combined Decision of Deep Learners," in IEEE Access, vol. 10, pp. 118198-118212, 2022.

A. Magdy, H. Hussein, R. F. Abdel-Kader and K. A. E. Salam, "Performance Enhancement of Skin Cancer Classification Using Computer Vision," in IEEE Access, vol. 11, pp. 72120-72133, 2023.

Q. U. Ain, H. Al-Sahaf, B. Xue and M. Zhang, "Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming," in IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 2727-2740, May 2023.

Z. Lan, S. Cai, X. He and X. Wen, "FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer," in IEEE Access, vol. 10, pp. 76261-76267, 2022.

A. K. Sharma et al., "Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network," in IEEE Access, vol. 10, pp. 17920-17932, 2022.

I. Razzak and S. Naz, "Unit-Vise: Deep Shallow Unit-Vise Residual Neural Networks With Transition Layer For Expert Level Skin Cancer Classification," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 2, pp. 1225-1234, 1 March-April 2022.

Premier Surgical Staff. What Is the Difference between Melanoma And non-Melanoma Skin Cancer? PSS. Available online: (accessed on 6 February 2021)!/topWithHeader/wideContentTop/main




How to Cite

Chitralekha Dwivedi, Ritu Patidar, Sanjay Patidar, U. S. K. P. . (2024). Machine Learning Mastery: Leveraging Convolutional Neural Networks to Classify Skin Cancers as Benign or Malignant in the ISIC Database. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 328–336. Retrieved from



Research Article

Similar Articles

You may also start an advanced similarity search for this article.