Guerra-Rosas, E.; López-Ávila, L.F.; Garza-Flores, E.; Vidales-Basurto, C.A.; Álvarez-Borrego, J. Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures. Appl. Sci.2023, 13, 11425.
Guerra-Rosas, E.; López-Ávila, L.F.; Garza-Flores, E.; Vidales-Basurto, C.A.; Álvarez-Borrego, J. Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures. Appl. Sci. 2023, 13, 11425.
Guerra-Rosas, E.; López-Ávila, L.F.; Garza-Flores, E.; Vidales-Basurto, C.A.; Álvarez-Borrego, J. Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures. Appl. Sci.2023, 13, 11425.
Guerra-Rosas, E.; López-Ávila, L.F.; Garza-Flores, E.; Vidales-Basurto, C.A.; Álvarez-Borrego, J. Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures. Appl. Sci. 2023, 13, 11425.
Abstract
Eight lesions were analyzed using some algorithms of Intelligence Artificial: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASC). This manuscript presents the possibility of using concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eleven other Artificial Intelligence models were applied so that eight skin lesions could be classified by analyzing the particular signatures of each lesion. The database was randomly divided into 80%–20% for the training and test datasets images, respectively. The metrics that are being reported are accuracy, sensitivity, specificity, and precision. Each case was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the average and ±standard deviation were reported. Although all the results were very satisfactory, the best average mark for the eight lesions analyzed was obtained using the Subspace KNN model, where the metrics for the test were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
