Modified LSTM based skin lesion segmentation and classification using optimization concept

Abstract

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%.