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Modified LSTM based skin lesion segmentation and classification using optimization concept

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Publicado en:SN Applied Sciences vol. 7, no. 8 (Aug 2025), p. 880
Autor principal: Gomathi, S.
Otros Autores: Arunachalam, N.
Publicado:
Springer Nature B.V.
Materias:
Dermatology
Neurons
Tomography
Accuracy
Comparative analysis
Datasets
Deep learning
Skin cancer
Classification
Visual discrimination learning
Hair
Optimization techniques
Segmentation
Medical imaging
Lesions
Image processing
Skin diseases
Registration
Automation
Machine learning
Clinical outcomes
Skin lesions
Image analysis
Image segmentation
Cancer
Decision making
Microscopy
Hair removal
Digital photography
Environmental
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Resumen:Skin cancer, one of the most serious types of cancer, affects a significant portion of the population. Image analysis has greatly enhanced automatic diagnostic accuracy compared to unaided visual assessment. Machine learning has emerged as a critical technique for automated skin lesion classification; however, its scalability is often constrained by the availability of high-quality annotated data for training. This research aims to perform segmentation and classification of skin lesions using novel deep learning techniques. Data samples were obtained from benchmark datasets, including HAM10000 and ISIC 2017, ensuring representativeness and diversity. Pre-processing involved hair removal followed by median filtering on the hair-removed images. Skin lesion segmentation was performed using the U-Net method, and features such as color, texture via GLCM, and RGB histogram features were extracted from the segmented images. The final classification phase utilized MLSTM with hidden neurons optimized using STBO, aiming to maximize accuracy and precision. The proposed model categorizes skin lesions as normal, benign, or malignant. The final classification phase utilized MLSTM with hidden neurons optimized using STBO, aiming to maximize accuracy and precision. The proposed model categorizes skin lesions as normal, benign, or malignant. Comparative analysis demonstrated that the MLSTM-STBO model achieves an accuracy of 97.20%, sensitivity of 97.14%, precision of 97.04%, specificity of 99.48%, F1-score of 97.08%, MCC of 98.12%, TPR of 96.17%, and FPR of 6.20%, outperforming traditional methods by margins up to 25.21%.
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-025-07511-2
Fuente:Science Database