  Skin lesion segmentation plays a crucial role in the computer-aided diagnosis
of melanoma. Deep Learning models have shown promise in accurately segmenting
skin lesions, but their widespread adoption in real-life clinical settings is
hindered by their inherent black-box nature. In domains as critical as
healthcare, interpretability is not merely a feature but a fundamental
requirement for model adoption. This paper proposes IARS SegNet an advanced
segmentation framework built upon the SegNet baseline model. Our approach
incorporates three critical components: Skip connections, residual
convolutions, and a global attention mechanism onto the baseline Segnet
architecture. These elements play a pivotal role in accentuating the
significance of clinically relevant regions, particularly the contours of skin
lesions. The inclusion of skip connections enhances the model's capacity to
learn intricate contour details, while the use of residual convolutions allows
for the construction of a deeper model while preserving essential image
features. The global attention mechanism further contributes by extracting
refined feature maps from each convolutional and deconvolutional block, thereby
elevating the model's interpretability. This enhancement highlights critical
regions, fosters better understanding, and leads to more accurate skin lesion
segmentation for melanoma diagnosis.
