ABT-GAMNet: A novel adaptive Boundary-aware transformer with Gated attention mechanism for automated skin lesion segmentation

Segmentation of skin lesions is the hotspot development process in the medical field. As skin cancer becomes most disastrous for humans, it requires framing an effective model for identifying the lesion region over the skin. As represented in the image format, implementing the decision-based system is quite challenging. Owing to the noise and artifacts in images, it becomes cumbersome to segment the exact lesion regions over the skin images. In recent years, the deep learning model is used to attain the expected results. Yet, it still subsists with overfitting issues and large-scale dimensional datasets, it mitigates the segmentation results. To meet up the pre-requisite, a novel adaptive-based segmentation technique is proposed for skin lesion segmentation. In the first stage, the source images are fetched from the reputed skin image data sources. It is followed by pre-processing stage. In the second phase, the pre-processing is done in two ways: (i) filtering methods and (ii) adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE). In the adaptive CLAHE model, the threshold value is optimized by proposing the new hybrid algorithm as Hybrid Rat Electric Fish Swarm Optimization (HREFSO) for enhancing the contrast level in pre-processed images. Subsequently, the pre-processed images are fed into the novel Adaptive Boundary-aware Transformer with Gated Attention Mechanism (ABT-GAM) for segmenting the lesion regions in images. To improve the segmentation accuracy, the parameters are determined optimally using HREFSO. The evaluation is performed and validated across various metrics. Hence, the outcome elucidates that it acquires a higher segmentation accuracy for diagnosing skin cancer.