Optimized Skin Lesion Segmentation: Analysing DeepLabV3+ and ASSP Against Generative AI-Based Deep Learning Approach

Abstract

Accurate skin lesion segmentation is an important task in dermatology for facilitating early diagnosis and treatment planning. The challenges in skin lesion segmentation comprehend the variability in lesion, low contrast, heterogeneous backgrounds, overlapping or connected lesions, noise and certain artifacts. Despite of these challenges, Deep learning models accomplish remarkable results for skin lesion segmentation by automatically learning discriminative features. The current research introduces a novel approach utilizing the ASSP-based Deeplabv3+ for skin lesion segmentation along with other UNET-based learners while employing VGG-16, VGG-19 and Dense nets as encoders. In addition, an analysis is conducted on GAN-UNET to evaluate the potential of Generative Artificial Intelligence in generating segmented images of skin lesions. Three benchmark medical image datasets, namely ISIC-2016, ISIC-2018, and HAM10000 Lesion Boundary Segmentation, are used to evaluate all five models. The models are trained exclusively on the ISIC-2018 dataset. A comparative analysis is performed, comparing the performance of these models against state-of-the-art segmentation methods, focusing on standard computer vision metrics. The proposed Deeplabv3+ model outperforms by showcasing its ability to accurately delineate skin lesions and surpassing existing techniques in terms of segmentation accuracy as 0.97, Jaccard coefficient as 0.84 and dice coefficient as 0.91.