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While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold ($\\rm {\\mathit{n}_{s}}$), to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes $\\rm {\\mathit{P}_{mean}}$ and $\\rm {IC_{mean}}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25\u00a0per\u2009cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores ($\\rm {\\mathit{P}_{mean}}$), filtering based on Information Content ($\\rm {IC_{mean}}$), and segmentation score ($\\rm {n_{s}}$). 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