Application of Convolutional Neural Networks in the Automatic Detection of Cutaneous Melanoma
Keywords:
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.
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[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.
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