An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics.

The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results.

A Hybrid Preprocessor DE-ABC for Efficient Skin-Lesion Segmentation with Improved Contrast.

Rapid advancements and the escalating necessity of autonomous algorithms in medical imaging require efficient models to accomplish tasks such as segmentation and classification. However, there exists a significant dependency on the image quality of datasets when using these models. Appreciable improvements to enhance datasets for efficient image analysis have been noted in the past.

A novel hybrid meta-heuristic contrast stretching technique for improved skin lesion segmentation.

The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest.