Application of Convolutional Neural Networks in the Automatic Detection of Cutaneous Melanoma
DOI:
https://doi.org/10.33333/lajc.vol13n1.08Keywords:
Convolutional Neural Networks, Melanoma, Deep Learning, Preprocessing images, Automatic diagnosticAbstract
Early diagnosis of melanoma is crucial for improving survival rates, which has driven the development of deep learning models for its automated detection. This research aims to evaluate the performance of a convolutional neural network (CNN) in classifying dermoscopic images of skin lesions, comparing its accuracy with that of dermatology experts. To achieve this, a CNN was trained using a set of images that were preprocessed to improve the generalization ability of the model. The evaluation was carried out by means of quality metrics such as accuracy, precision, sensitivity, and F1-score. In addition, the ROC curve and confusion matrix were used to analyze the balance between false positives and false negatives in the classification. The results showed that the CNN outperformed dermatologists in terms of specificity and sensitivity, with an area under the curve (AUC) close to 1, indicating high discriminatory power. The confusion matrix revealed that the classification was correct in most cases, minimizing type I and type II errors. In conclusion, the implementation of neural networks in melanoma diagnosis represents a promising tool for medical care. However, opportunities for improvement were identified, such as adjusting decision thresholds and optimizing image preprocessing, which will increase the accuracy of the model in future clinical applications.
Downloads
References
[2] S. P. Kothapalli, P. S. H. Priya, V. S. Reddy, B. Lahya, and P. Ragam, “Melanoma Skin Cancer Detection using SVM and CNN,” EAI Endorsed Trans Pervasive Health Technol, vol. 9, no. 1, May 2023, doi: 10.4108/eetpht.9.4340.
[3] F. Yalcinkaya and A. Erbas, “Convolutional neural network and fuzzy logic-based hybrid melanoma diagnosis system,” Elektronika ir Elektrotechnika, vol. 27, no. 2, pp. 69–77, 2021, doi: 10.5755/j02.eie.28843.
[4] Alexander Scarlat MD, “Dataset Melanoma,” Kaggle. Accessed: Feb. 20, 2025. [Online]. Available: https://www.kaggle.com/datasets/drscarlat/melanoma
[5] T. Lee, V. Ng, R. Gallagher, A. Coldman, and D. McLean, “Dullrazor®: A software approach to hair removal from images,” Comput Biol Med, vol. 27, no. 6, pp. 533–543, 1997, doi: https://doi.org/10.1016/S0010-4825(97)00020-6.
[6] C. H. Chen, “Adaptive Image Filtering,” Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing, pp. 19–31, Jan. 2000, doi: 10.1016/B978-012077790-7/50005-9.
[7] Keras, “The Sequential class.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/models/sequential/
[8] Keras, “Conv2D layer.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/convolution_layers/convolution2d/
[9] Keras, “BatchNormalization layer.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/normalization_layers/batch_normalization/
[10] Keras, “Layer activation functions - ReLu .” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/activations/#relu-function
[11] Keras, “MaxPooling2D layer.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/pooling_layers/max_pooling2d/
[12] Keras, “Flatten layer.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/reshaping_layers/flatten/
[13] Keras, “Dense layer.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/core_layers/dense/
[14] Keras, “Layer activation functions | SoftMax.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/layers/activations/#softmax-function
[15] Keras, “Adam.” Accessed: May 13, 2025. [Online]. Available: https://keras.io/api/optimizers/adam/
[16] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Dec. 2014, [Online]. Available: http://arxiv.org/abs/1412.6980
[17] Keras, “EarlyStopping.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/callbacks/early_stopping/
[18] Keras, “ReduceLROnPlateau.” Accessed: Feb. 20, 2025. [Online]. Available: https://keras.io/api/callbacks/reduce_lr_on_plateau/
[19] Google Cloud, “Introducción a Cloud TPU.” Accessed: Feb. 20, 2025. [Online]. Available: https://cloud.google.com/tpu/docs/intro-to-tpu?hl=es-419
[20] R. C. Maron et al., “Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks,” Eur J Cancer, vol. 119, pp. 57–65, Sep. 2019, doi: 10.1016/j.ejca.2019.06.013.
[21] H. A. Haenssle et al., “Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions,” Annals of Oncology, vol. 31, no. 1, pp. 137–143, Jan. 2020, doi: 10.1016/j.annonc.2019.10.013.
[22] W. Salma and A. S. Eltrass, “Automated deep learning approach for classification of malignant melanoma and benign skin lesions,” Multimed Tools Appl, vol. 81, no. 22, pp. 32643–32660, Sep. 2022, doi: 10.1007/s11042-022-13081-x.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 José Alberto León Alarcón, Roly Steeven Cedeño Menéndez
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Notice
Authors who publish this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non-Commercial-Share-Alike 4.0 International 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Disclaimer
LAJC in no event shall be liable for any direct, indirect, incidental, punitive, or consequential copyright infringement claims related to articles that have been submitted for evaluation, or published in any issue of this journal. Find out more in our Disclaimer Notice.
