IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation

Skin lesion segmentation plays a crucial role in the computer-aided diagnosisof melanoma. Deep Learning models have shown promise in accurately segmentingskin lesions, but their widespread adoption in real-life clinical settings ishindered by their inherent black-box nature. In domains as critical ashealthcare, interpretability is not merely a feature but a fundamentalrequirement for model adoption. This paper proposes IARS SegNet an advancedsegmentation framework built upon the SegNet baseline model. Our approachincorporates three critical components: Skip connections, residualconvolutions, and a global attention mechanism onto the baseline Segnetarchitecture. These elements play a pivotal role in accentuating thesignificance of clinically relevant regions, particularly the contours of skinlesions. The inclusion of skip connections enhances the model's capacity tolearn intricate contour details, while the use of residual convolutions allowsfor the construction of a deeper model while preserving essential imagefeatures. The global attention mechanism further contributes by extractingrefined feature maps from each convolutional and deconvolutional block, therebyelevating the model's interpretability. This enhancement highlights criticalregions, fosters better understanding, and leads to more accurate skin lesionsegmentation for melanoma diagnosis.