Deep Learning-Based Approach for Skin Cancer Detection

Jhony; Shweta Sinha

doi:10.55524/ijirem.2025.12.2.19

Abstract

Melanoma skin cancer is one of the fastest growing skin cancer types worldwide and remains one of the most challenging public health issues to manage. Mortality rates can be decreased with timely and precise treatment, and so the early-stage identification of skin cancer is still a vital need. In this paper, we develop a skin lesion classification system that utilizes deep learning techniques on dermoscopic images. We built a custom convolutional neural network (CNN) with TensorFlow and Keras that performs binary classification on malignant and benign skin lesions. Each dermoscopic image is subjected to resizing to a resolution of 224 x 224 pixels and subsequent steps of preprocessing, which include normalization as well as data augmentation, to improve model generalization. The proposed CNN architecture consists of three “convolutional pooling” blocks followed by fully connected dense layers, thus achieving high accuracy and equally high-performance metrics on a balanced dataset. The model produced robust evaluation results—with the accuracy, precision, and recall alongside F1-score and AUC-ROCs marking the metrics of strength—confirming these claims. The deep learning methods, as discussed here, can potentially support medical decision-making in dermatology at glance, and this potential continuously needs further research. After that, plans include working on transfer learning, multimodal inputs, and moving the system to mobile devices for real-time diagnosis.

Keywords

Skin Cancer Detection, Deep Learning, CNN, Dermoscopic Images, TensorFlow, Medical Image Classification, Computer-Aided Diagnosis

References

[1] A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017. Available from: https://doi.org/10.1038/nature21056

[2] J. Kawahara et al., "Seven-point checklist and skin lesion classification using multitask multi-label CNNs," in Proc. IEEE ISBI, 2016. Available from: https://doi.org/10.1109/jbhi.2018.2824327

[3] Q. Abbas et al., "A lightweight CNN model for skin lesion classification," Sensors, vol. 21, no. 11, p. 3721, 2021. Available from: http://dx.doi.org/10.1016/j.asoc.2024.111794

[4] J. Almaraz-Damian et al., "A modular CNN architecture for Melanoma Classification," Computers in Biology and Medicine, vol. 123, p. 103855, 2020. Available from: https://shorturl.at/M5XX4

[5] N. Gessert et al., "Skin Lesion Classification Using CNNs with Batch and Weight Normalization," IEEE Access, vol. 7, pp. 114142-114153, 2019. Available from: https://arxiv.org/pdf/1905.02793

[6] Q. Abbas et al., “A lightweight CNN model for skin lesion classification,” Sensors, vol. 21, no. 11, p. 3721, 2021.Available from: http://dx.doi.org/10.1016/j.asoc.2024.111794

[7] J. Almaraz-Damian et al., “A modular CNN architecture for melanoma classification,” Computers in Biology and Medicine, vol. 123, p. 103855, 2020. Available from: https://shorturl.at/M5XX4

[8] Kaggle. Challenges in representation learning: Facial expression recognition challenge, 2013. Available from: http://arxiv.org/abs/1307.0414

[9] P. Tschandl, C. Rosendahl, and H. Kittler, "The HAM10000 dataset: A large collection of multi-source dermoscopic images of pigmented skin lesions," 2018. Available from: https://doi.org/10.1038/sdata.2018.161

[10] D. P. Kingma and J. Ba, "Adam: An Approach for Stochastic Optimization," in International Conference on Learning Representations (ICLR), 2015. Available from: https://arxiv.org/pdf/1412.6980

[11] Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)," 2018. Available from: https://doi.org/10.48550/arXiv.1902.03368

[12] C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," 2019. Available from: https://doi.org/10.1186/s40537-019-0197-0

[13] N. Codella et al., "Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)," 2019. Available from: https://doi.org/10.48550/arXiv.1710.05006

Cites this article as

Jhony, S. Sinha, "Deep Learning-Based Approach for Skin Cancer Detection", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-12, Issue-2, Page No-119-123, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.2.19