Derechos de autor 2025 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Clasificación de cáncer de piel en tiempo real: Implementación de una CNN basada en descriptores de datos en hardware embebido
Corresponding Author(s) : Carlos Vicente Niño Rondón
Investigación e Innovación en Ingenierías,
Vol. 13 Núm. 2 (2025): Julio - Diciembre
Resumen
Objetivo: Desarrollar un sistema de detección de cáncer de piel implementado en un dispositivo embebido Raspberry Pi 4B, capaz de analizar imágenes del conjunto de datos HAM10000 para facilitar la detección temprana y mejorar el tratamiento de los pacientes. Metodología: Se realizó un análisis exhaustivo del conjunto de datos, se desarrolló un modelo de aprendizaje profundo y se creó una herramienta de diagnóstico asistido con una interfaz gráfica de usuario para mejorar la usabilidad. El modelo fue entrenado durante 50 épocas, optimizando su rendimiento para su implementación en un sistema embebido de baja potencia. Resultados: El modelo propuesto alcanzó una precisión de validación del 96.30%, demostrando alta eficiencia en tiempos de ejecución, uso de CPU y RAM, además de mantener la temperatura del procesador dentro de límites seguros. En comparación con otras técnicas analizadas, este modelo mantiene un rendimiento superior en todos los parámetros clave, lo que indica su fiabilidad y eficacia en las tareas de clasificación. Conclusiones: La implementación exitosa del modelo en un dispositivo embebido de baja potencia resalta su potencial para mejorar la tecnología médica portátil, permitiendo diagnósticos accesibles y en tiempo real en diversos entornos clínicos. Se recomienda realizar futuras investigaciones para optimizar aún más el modelo y ampliar su aplicación en el ámbito médico.
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Referencias
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[2] N. Razmjooy et al., “Computer-aided Diagnosis of Skin Cancer: A Review,” Current Medical Imaging Formerly Current Medical Imaging Reviews, vol. 16, no. 7, pp. 781–793, Jan. 2020, doi: 10.2174/1573405616666200129095242.
[3] A. Kumar and A. Vatsa, “Untangling Classification Methods for Melanoma Skin Cancer,” Front Big Data, vol. 5, no. March, pp. 1–11, 2022, doi: 10.3389/fdata.2022.848614.
[4] C. Jen Ngeh, C. Ma, T. Kuan-Wei Ho, Y. Wang, and J. Raiti, “Deep Learning on Edge Device for Early Prescreening of Skin Cancers in Rural Communities,” in 2020 IEEE Global Humanitarian Technology Conference, GHTC 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/GHTC46280.2020.9342911.
[5] K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neuroscience Informatics, vol. 2, no. 4, p. 100034, Dec. 2022, doi: 10.1016/j.neuri.2021.100034.
[6] W. J. Sarmiento, C. Sturm, and C. A. Collazos, “Desafíos en la Interacción Humano-Computador desde una mirada retrospectiva: Una reflexión global con énfasis en Latinoamérica,” TecnoLógicas, vol. 27, no. 60, pp. 1–6, 2024, [Online]. Available: https://hci-collab.uxartetic.com/
[7] R. P. Widhianto, W. Priharti, F. Alia, A. R. Alfariyani, Z. A. Al-Hamid, and E. K. Sutedja, “Hardware Design of Skin Cancer Detection Device,” in 9th International Conference on Engineering and Emerging Technologies (ICEET), Institute of Electrical and Electronics Engineers (IEEE), May 2024, pp. 1–6. doi: 10.1109/iceet60227.2023.10525755.
[8] P. Osipovs, D. Bliznuks, and I. Kuzmina, “Cloud Infrastructure for Skin Cancer Scalable Detection System,” in 2018 Advances in Wireless and Optical Communications, 2018, pp. 1–5.
[9] P. F. Durães and M. P. Véstias, “Smart Embedded System for Skin Cancer Classification,” Future Internet, vol. 15, no. 2, Feb. 2023, doi: 10.3390/fi15020052.
[10] J. Verstockt, S. Verspeek, F. Thiessen, W. A. Tjalma, L. Brochez, and G. Steenackers, “Skin Cancer Detection Using Infrared Thermography: Measurement Setup, Procedure and Equipment,” May 01, 2022, MDPI. doi: 10.3390/s22093327.
[11] S. Jiang, H. Li, and Z. Jin, “A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis,” IEEE J Biomed Health Inform, vol. 25, no. 5, pp. 1483–1494, 2021, doi: 10.1109/JBHI.2021.3052044.
[12] M. Nauta, R. Walsh, A. Dubowski, and C. Seifert, “Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis,” Diagnostics, vol. 12, no. 1, Jan. 2022, doi: 10.3390/diagnostics12010040.
[13] I. Kousis, I. Perikos, I. Hatzilygeroudis, and M. Virvou, “Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application,” Electronics (Switzerland), vol. 11, no. 9, May 2022, doi: 10.3390/electronics11091294.
[14] N. Kausar et al., “Multiclass skin cancer classification using ensemble of fine-tuned deep learning models,” Applied Sciences (Switzerland), vol. 11, no. 22, Nov. 2021, doi: 10.3390/app112210593.
[15] T. Saba, “Computer vision for microscopic skin cancer diagnosis using handcrafted and non-handcrafted features,” Jun. 01, 2021, John Wiley and Sons Inc. doi: 10.1002/jemt.23686.
[16] S. Afifi, H. GholamHosseini, and R. Sinha, “A system on chip for melanoma detection using FPGA-based SVM classifier,” Microprocess Microsyst, vol. 65, pp. 57–68, Mar. 2019, doi: 10.1016/j.micpro.2018.12.005.
[17] J. O. Emuoyibofarhe and D. Ajisafe, “Early Skin Cancer Detection Using Deep Convolutional Neural Networks on Mobile Smartphone,” International Journal of Information Engineering and Electronic Business, vol. 12, no. 2, pp. 21–27, Apr. 2020, doi: 10.5815/ijieeb.2020.02.04.
[18] P. Tschandl, C. Rosendahl, and H. Kittler, “Data descriptor: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Sci Data, vol. 5, pp. 1–9, 2018, doi: 10.1038/sdata.2018.161.
[19] H. L. Gururaj, N. Manju, A. Nagarjun, V. N. Manjunath Aradhya, and F. Flammini, “DeepSkin: A Deep Learning Approach for Skin Cancer Classification,” IEEE Access, vol. 11, pp. 50205–50214, 2023, doi: 10.1109/ACCESS.2023.3274848.
[20] S. Wang and M. Hamian, “Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization,” Comput Intell Neurosci, vol. 2021, 2021, doi: 10.1155/2021/9528664.
[21] N. Onizawa, S. C. Smithson, B. H. Meyer, W. J. Gross, and T. Hanyu, “In-Hardware Training Chip Based on CMOS Invertible Logic for Machine Learning,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 5, pp. 1541–1550, May 2020, doi: 10.1109/TCSI.2019.2960383.
[22] H. Fujita, “AI-based computer-aided diagnosis (AI-CAD): the latest review to read first,” Radiol Phys Technol, vol. 13, no. 1, pp. 6–19, 2020, doi: 10.1007/s12194-019-00552-4.
[23] L. Hoang, S. H. Lee, E. J. Lee, and K. R. Kwon, “Multiclass Skin Lesion Classification Using a Novel Lightweight Deep Learning Framework for Smart Healthcare,” Applied Sciences (Switzerland), vol. 12, no. 5, Mar. 2022, doi: 10.3390/app12052677.
[24] A. Mehmood, Y. Gulzar, Q. M. Ilyas, A. Jabbari, M. Ahmad, and S. Iqbal, “SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions,” Cancers (Basel), vol. 15, no. 14, Jul. 2023, doi: 10.3390/cancers15143604.
[25] F. Afza, M. Sharif, M. Mittal, M. A. Khan, and D. Jude Hemanth, “A hierarchical three-step superpixels and deep learning framework for skin lesion classification,” Methods, vol. 202, pp. 88–102, Jun. 2022, doi: 10.1016/j.ymeth.2021.02.013.
[26] A. Bibi et al., “Skin lesion segmentation and classification using conventional and deep learning based framework,” Computers, Materials and Continua, vol. 71, no. 2, pp. 2477–2495, 2022, doi: 10.32604/cmc.2022.018917.
[27] F. Afza, M. Sharif, M. A. Khan, U. Tariq, H. S. Yong, and J. Cha, “Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine,” Sensors, vol. 22, no. 3, Feb. 2022, doi: 10.3390/s22030799.
[28] S. Jiang, H. Li, and Z. Jin, “A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis,” IEEE J Biomed Health Inform, vol. 25, no. 5, pp. 1483–1494, May 2021, doi: 10.1109/JBHI.2021.3052044.
[29] M. Saeed, A. Naseer, H. Masood, S. ur Rehman, and V. Gruhn, “The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach,” IEEE Access, pp. 1–1, Nov. 2023, doi: 10.1109/access.2023.3332628.
[30] X. Zhou et al., “CUSS-Net: A Cascaded Unsupervised-Based Strategy and Supervised Network for Biomedical Image Diagnosis and Segmentation,” IEEE J Biomed Health Inform, vol. 27, no. 5, pp. 2444–2455, May 2023, doi: 10.1109/JBHI.2023.3238726.
[31] T. C. Pham, A. Doucet, C. M. Luong, C. T. Tran, and V. D. Hoang, “Improving Skin-Disease Classification Based on Customized Loss Function Combined with Balanced Mini-Batch Logic and Real-Time Image Augmentation,” IEEE Access, vol. 8, pp. 150725–150737, 2020, doi: 10.1109/ACCESS.2020.3016653.
[32] H. L. Gururaj, N. Manju, A. Nagarjun, V. N. Manjunath Aradhya, and F. Flammini, “DeepSkin: A Deep Learning Approach for Skin Cancer Classification,” IEEE Access, vol. 11, pp. 50205–50214, 2023, doi: 10.1109/ACCESS.2023.3274848.